Location tracking based on minimap

Abstract

This disclosure relates to location tracking based on minimaps. Apparatus, systems, and techniques for determining location information for a gaming session. In at least one embodiment, the location information is determined by matching at least features extracted from the minimap with features extracted from the map, and determining a transformation matrix to be applied to the location in the minimap.

Description

Background Technology

[0001] As video games and other content and uses evolve, the way content is used and delivered needs to be adjusted accordingly. For example, gameplay videos are increasingly being shared with large audiences, and activities such as esports are changing the way online games are developed and content is presented to viewers. In many cases, these rapid changes result in content that is not optimal for its intended purpose, or requires significant human intervention and additional resources to create engaging content. Besides the increased resources, this human intervention often increases the amount of time required before the content is presented to viewers or gamers. Attached Figure Description

[0002] Figure 1 An example is shown of extracting features from a map and a minimap to determine location information according to at least one embodiment;

[0003] Figure 2 An example is shown of comparing features extracted from a map and a minimap according to at least one embodiment to determine location information;

[0004] Figure 3 An example is shown that, according to at least one embodiment, motion is tracked based at least in part on a set of minimaps;

[0005] Figure 4 An example is shown that location information is determined at least in part based on local location estimation according to at least one embodiment;

[0006] Figure 5 An example is shown that, according to at least one embodiment, location information is determined based at least in part on a homography matrix.

[0007] Figure 6 An example process for determining location information within a game environment according to at least one embodiment is shown;

[0008] Figure 7 These are example system diagrams for a game streaming system according to some embodiments of this disclosure;

[0009] Figure 8A The inference and / or training logic according to at least one embodiment is illustrated;

[0010] Figure 8B The inference and / or training logic according to at least one embodiment is illustrated;

[0011] Figure 9 The training and deployment of a neural network according to at least one embodiment are illustrated;

[0012] Figure 10An example data center system according to at least one embodiment is shown;

[0013] Figure 11A An example of an autonomous vehicle according to at least one embodiment is shown;

[0014] Figure 11B The illustration shows an embodiment according to at least one of the embodiments. Figure 11A Examples of camera positions and field of view for autonomous vehicles;

[0015] Figure 11C This is an illustration based on at least one embodiment. Figure 11A A block diagram of an example system architecture for an autonomous vehicle;

[0016] Figure 11D The illustration, according to at least one embodiment, is for one or more cloud-based servers and Figure 11A A diagram of a system for communication between autonomous vehicles;

[0017] Figure 12 This is a block diagram illustrating a computer system according to at least one embodiment;

[0018] Figure 13 This is a block diagram illustrating a computer system according to at least one embodiment;

[0019] Figure 14 A computer system according to at least one embodiment is shown;

[0020] Figure 15 A computer system according to at least one embodiment is shown;

[0021] Figure 16A A computer system according to at least one embodiment is shown;

[0022] Figure 16B A computer system according to at least one embodiment is shown;

[0023] Figure 16C A computer system according to at least one embodiment is shown;

[0024] Figure 16D A computer system according to at least one embodiment is shown;

[0025] Figure 16E and Figure 16F A shared programming model according to at least one embodiment is shown;

[0026] Figure 17 An exemplary integrated circuit and a related graphics processor according to at least one embodiment are shown;

[0027] Figure 18A and Figure 18B An exemplary integrated circuit and an associated graphics processor according to at least one embodiment are shown;

[0028] Figure 19A and Figure 19B Additional exemplary graphics processor logic according to at least one embodiment is shown;

[0029] Figure 20 A computer system according to at least one embodiment is shown;

[0030] Figure 21A A parallel processor according to at least one embodiment is shown;

[0031] Figure 21B A partitioning unit according to at least one embodiment is shown;

[0032] Figure 21C A processing cluster according to at least one embodiment is shown;

[0033] Figure 21D A graphics multiprocessor according to at least one embodiment is shown;

[0034] Figure 22 A multi-graphics processing unit (GPU) system according to at least one embodiment is illustrated;

[0035] Figure 23 A graphics processor according to at least one embodiment is shown;

[0036] Figure 24 It is a block diagram illustrating a processor microarchitecture for a processor according to at least one embodiment;

[0037] Figure 25 A deep learning application processor according to at least one embodiment is shown;

[0038] Figure 26 A block diagram of an example neuromorphic processor is shown according to at least one embodiment;

[0039] Figure 27 At least a portion of a graphics processor according to one or more embodiments is shown;

[0040] Figure 28 At least a portion of a graphics processor according to one or more embodiments is shown;

[0041] Figure 29 At least a portion of a graphics processor according to one or more embodiments is shown;

[0042] Figure 30 It is a block diagram of a graphics processing engine of a graphics processor according to at least one embodiment;

[0043] Figure 31 It is a block diagram of at least a portion of a graphics processor core according to at least one embodiment;

[0044] Figure 32A and Figure 32B The diagram illustrates thread execution logic according to at least one embodiment, which includes an array of processing elements of a graphics processor core.

[0045] Figure 33 A parallel processing unit (“PPU”) according to at least one embodiment is shown;

[0046] Figure 34 A general-purpose processing cluster (“GPC”) according to at least one embodiment is illustrated;

[0047] Figure 35 A memory partition unit of a parallel processing unit (“PPU”) according to at least one embodiment is shown;

[0048] Figure 36 A streaming multiprocessor according to at least one embodiment is illustrated;

[0049] Figure 37 This is an example data flow diagram of an advanced computing pipeline according to at least one embodiment;

[0050] Figure 38 This is a system diagram of an example system for training, adapting, instantiating, and deploying machine learning models in an advanced computing pipeline, according to at least one embodiment;

[0051] Figure 39 Example illustrations of an advanced computing pipeline for processing imaging data according to at least one embodiment;

[0052] Figure 40A Includes example data flow diagrams of virtual instruments supporting ultrasound equipment according to at least one embodiment;

[0053] Figure 40B Includes example data flow diagrams of virtual instruments supporting CT scanners according to at least one embodiment;

[0054] Figure 41A A data flow diagram illustrating the process for training a machine learning model according to at least one embodiment is shown; and

[0055] Figure 41B This is an example illustration of a client-server architecture that utilizes a pre-trained annotation model to enhance an annotation tool, according to at least one embodiment. Detailed Implementation

[0056] Embodiments of this disclosure relate to determining a player's location in a game environment based at least in part on a sub-interface or a dedicated panel commonly referred to as a "minimap" displayed during a game session, and typically depicting a miniaturized subset of a map corresponding to the location of the user's avatar or otherwise representing the user's location in the virtual game environment. Systems and methods are disclosed that perform feature extraction on a map (e.g., a two-dimensional image representing a game area executed by an application) and a minimap corresponding to a portion of the map to determine a location (e.g., a player's location) within the game area represented by the map. For example, features extracted from the minimap are compared with features extracted from the map, and then the location is determined using homography techniques with matching features. This location information can then be used for various purposes. In one example, the location information is used to analyze events that occur during game execution (e.g., by the application and / or game engine). In another example, the location information is used to generate a timeline of the game session that includes location and / or other information.

[0057] To determine location information, systems and methods described below in more detail, such as determining a map for a gaming session, are available. According to this disclosure, various techniques can be used to determine a map for a specific gaming session. For example, images of the gaming session can be captured and analyzed to detect icons or other information indicating a map associated with the gaming session. Once the map is determined, it can be retrieved, for example, from images of the gaming session, storage locations, application programming interfaces (APIs), or other data representing the map.

[0058] Furthermore, in various examples, one or more feature extraction techniques can be applied to the map to obtain a feature set corresponding to the map. Various feature extraction techniques, such as Scale Invariant Feature Transform (SIFT), Speed-Up Robust Features (SURF), Robust Independent Fundamental Features (BRIEF), Oriented Fast and / or Rotated BRIEF (ORB) techniques, can be used in conjunction with this disclosure, as described in more detail below. In one example, this feature set is extracted from a map, and these features comprise a matrix representing the values ​​of the features extracted from the map. This information can be saved and reused in future game sessions (e.g., features extracted from a specific map).

[0059] Furthermore, minimaps can be extracted from images and / or videos captured during a gameplay session. For example, a subset of frames and / or images in a gameplay session may contain a minimap representing the player's surroundings. After acquiring the minimap, feature extraction can be performed on it. Similar to the example described above, various feature extraction techniques can be applied, and a feature set of the minimap can be obtained. To determine location (e.g., the player's position within the map), in one example, the feature set obtained from the minimap is compared with a feature set obtained from the map.

[0060] For example, all combinations and / or permutations of a feature set obtained from a minimap are compared with a feature set obtained from a map to determine a specific or particular portion of the map corresponding to the minimap. Feature comparison can include various techniques, such as determining Euclidean distance, etc. 2 - Criteria or histogram-based metrics. For example, for a pair of features (e.g., a first feature extracted from a minimap and a second feature extracted from a map), the Euclidean distance is determined, and if the value of the Euclidean distance is greater than a threshold, the pair of features can be considered a good match.

[0061] In one example, location is determined using homography once a sufficient number of features are matched (e.g., features extracted from the minimap match features extracted from the map). Specifically, a homography matrix can be computed to transform a location within the minimap (e.g., the center of the minimap) into a location within the map (e.g., the player's global location within the map during a game session). In other examples, local motion (e.g., motion between consecutive frames of a game session) can be computed at least in part based on optical flow estimation. Returning to the example above, location can be determined at least in part based on scaling factors, translational motion, and rotation, as represented by the homography matrix. As mentioned above, this location information can be included in various end-user features, such as activity feeds and activity timelines that include location information.

[0062] Figure 1 An environment 100 for extracting features from a map and a minimap to determine location information, according to at least one embodiment, is illustrated. In various embodiments, the map 102 and / or the minimap 104 are extracted from a client device (e.g., client device 704, as described below). Figure 7(Described in more detail) This data is obtained from video data captured during a player's gaming session. For example, a client device executes an application (e.g., a game) that causes the client device's display to show a representation of the game environment with which it interacts through its game. In embodiments, a player (e.g., a user of the client device) plays a game, and user input is captured and used to interact with the game and / or game environment during the gaming session (e.g., moving, performing actions, interacting with objects and other players, etc.). In various embodiments, map 102 and minimap 104 include map data representing map 102 and / or minimap 104. For example, map data may include images extracted from the gaming session, metadata associated with map 102 and / or minimap 104, or any other data representation or otherwise defining map 102 and / or minimap 104.

[0063] As described in more detail below, in various embodiments, information obtained from the map and / or minimap 104 is used to determine location information (e.g., the player's location within the game environment). In one example, map 102 includes a two-dimensional representation of a three-dimensional space (e.g., a top-down view) with avatars or other player representations. In other examples, minimap 104 includes a two-dimensional representation of a three-dimensional space within the boundaries of a player's avatar or other representation. In various embodiments, map 102 includes any representation of the game environment (e.g., top-down, isometric, two-dimensional, three-dimensional, terrain-based, etc.) with any level of detail (e.g., information about players, objects, features, or other elements in the game environment). Although Figure 1 Map 102 is displayed as a square, but in various embodiments, map 102 can be other shapes, such as circles or irregular shapes.

[0064] Furthermore, in various embodiments, the minimap 104 includes an area displayed to the player during a game session, depicting information relative to map 102 (e.g., player location, objects, features, or other elements within the game environment). Additionally, according to at least one embodiment, the minimap 104 includes a subset of map 102. For example, the minimap 104 includes an area of ​​the game environment visible to the player. In another example, the minimap 104 includes an area within a radius of the player. Although... Figure 1The minimap 104 is shown as circular, but in various embodiments, the minimap 104 can be various other shapes, such as square or irregular shapes. In various embodiments, the scale of map 102 and minimap 104 is different. Furthermore, according to at least one embodiment, the minimap 104 can rotate as the player's position and / or orientation changes, or the minimap 104 can have a fixed orientation and rotate within the minimap 104 to represent a symbol of the player.

[0065] As described in more detail below, according to at least one embodiment, location information (e.g., player location) can be determined at least in part based on map 102 and / or minimap 104. Furthermore, in various embodiments, location information can be used during event analysis to generate and / or enhance broadcasts, replays, comments, coaching information, timelines, recaps, or other information associated with the game session. For example, in the case of online video hosting or streaming, location information is used to enhance broadcasts, as described in more detail below, and provided by a third-party content provider, such as... or Facebook In order to stream to remote viewers (e.g., using NVIDIA GeForce). or ).

[0066] In various embodiments, location information includes records of the player's location during intervals and / or the entire duration of the game session. In one example, the location information is limited to the player's role relative to the player participating in the game session. In other examples, the location information includes other roles, objects, and events in the game session. In embodiments, the location information includes a dataset comprising the tracked player and the player's X and Y positions (e.g., positions measured in Euclidean space as pixels on map 102) relative to the origin of map 102 (e.g., points within map 102 used to calculate location, such as the center or lower left corner of the map).

[0067] As described in more detail below, in at least one embodiment, a client device initiates a game session with a server. Furthermore, in various embodiments, the server broadcasts the game session to one or more additional client devices viewing the game session. In various embodiments, the server captures media from the game session (e.g., audio and video generated during the game session) and enhances that media to generate a modified broadcast.

[0068] As described above, in various embodiments, playing a game includes audio and / or video data captured from a game session. Furthermore, a game engine or other executable code (as a result of execution by one or more processors of a computer system) renders or otherwise generates the game (e.g., audio and / or video of a user playing a game). In various embodiments, the systems and methods described herein capture or otherwise obtain map 102 and / or minimap 104 from the audio and / or video data captured from the game session. For example, in the first part of a game session (e.g., lobby, game settings, chat, etc.), map 102 is displayed and captured, and in the second part of a game session (e.g., a match), minimap 104 is displayed in a corner of the player's display and captured. In other embodiments, map 102 and / or minimap 104 are obtained from a storage location and / or directly from the game engine.

[0069] Furthermore, in various embodiments, the game engine or other executable code streams or otherwise transmits audio and / or video generated as a result of a user playing the game via a network connection. For example, a client device executes a game application, captures game data (e.g., audio, video, user input, biometrics, etc.), and transmits the captured game data to a game server that captures map 102 and / or minimap 104 and performs other operations described in this disclosure. In other embodiments, the game server executes a game application and streams game data to the client device, which then feeds back user input data to the game server (e.g., a Game-as-a-Service configuration, such as NVIDIA). TM GeForce NOW TM In various embodiments, the client device also provides input data to the game server. In one example, input data includes any data captured by an input device or other device used to control and / or play the game application. For example, input data includes mouse and keyboard data, controller data, motion tracking data, biometric data, video data, audio data, or any other data that can be used to play the game. In various embodiments, the input data causes the player's character to perform actions (e.g., move) in the game environment.

[0070] In various embodiments, feature sets are extracted once map 102 and / or minimap 104 are acquired. In one example, a scale-invariant feature transform (SIFT) algorithm is used to obtain a first feature set from map 102 and a second feature set from minimap 104. In various embodiments, according to at least one embodiment, computer vision (CV) techniques including image data processing and matching techniques, such as SIFT, accelerated robust features (SURF), robust independent fundamental features (BRIEF), oriented FAST, KAZE, A-KAZE, and / or rotated BRIEF (ORB) techniques, can be used to match features extracted from minimap 104 with features extracted from map 102. In such embodiments, matching features extracted from minimap 104 with features extracted from map 102 is used to determine location information. In other embodiments, corner detection techniques and / or another image feature detection technique are used to determine a match between minimap 104 and a subset of map 102 represented by minimap 104.

[0071] like Figure 1 As shown, in this embodiment, the extracted features are represented by circles, with smaller circles representing weaker features and larger circles representing stronger features. In such an embodiment, features of map 102 and / or minimap 104 are identified, and the unique properties of map 102 relative to other maps are determined. Furthermore, in various embodiments, the features are defined by one-dimensional vectors. For example, Figure 1 The circles shown are representations of one-dimensional vectors generated by one or more feature extraction algorithms as described above. In an embodiment, a 16x16 neighborhood around a keypoint is taken and divided into 16 4x4 sub-blocks, where an 8-bin orientation histogram is created for each sub-block, creating 128 bin values ​​represented by vectors to form the keypoint descriptor. In various embodiments, the feature set extracted from map 102 (e.g., a set of vectors generated by the feature extraction algorithm) is stored and used for multiple game sessions. Furthermore, in various embodiments, the feature extraction algorithm is applied to multiple images and / or frames of the game session to obtain multiple feature sets of minimap 104. For example, for multiple frames of a broadcast of a game session (e.g., every frame, every other frame, etc.), feature sets are extracted from minimap 104 and compared with feature sets extracted from map 102 (e.g., previously extracted and stored feature sets).

[0072] Figure 2 An environment 200 for comparing features extracted from a map and a minimap to determine location information, according to at least one embodiment, is illustrated. In various embodiments, the map 202 includes a representation on the game environment, such as those described above. Figure 1The relevant map 102. In various embodiments, the minimap 204 includes a representation of a portion of the game environment, as described above. Figure 1 The mini-map 104 is described. As described above, in embodiments, map 202 and mini-map 204 are obtained from a game session. In one example, the computer system executes an application that enables the computer system to capture image data associated with the game session. Furthermore, in various embodiments, features are extracted from map 202 and mini-map 204.

[0073] For example, a feature matching algorithm (e.g., SIFT) is used to generate features. As described above, in one example, the SIFT descriptor comprises a three-dimensional spatial histogram of gradients of image data (e.g., images of map 202 and / or minimap 204) to characterize the appearance of key points (e.g., features). In various embodiments, the gradient at a pixel is a sample representing a three-dimensional fundamental feature vector formed by the pixel location and gradient direction. In such embodiments, as a result of the SIFT descriptor, the histogram representing the oriented gradient within the neighborhood can be used as a distance metric for feature matching. For example, if the distance between two features (e.g., a feature extracted from map 202 and a feature extracted from minimap 204) is within a certain range, the features can be considered identical (e.g., the feature extracted from minimap 204 is the same as the feature extracted from map 202). In another example, if the determined distance between two features is below a threshold, a computer system (e.g., game server 802, described in more detail below) determines that the two features are matched (e.g., representing the same elements of the game environment).

[0074] In various embodiments, the distance between two features is determined using Euclidean distance. Other methods for measuring or otherwise determining distance can be used in conjunction with various embodiments, such as... 2 - Criteria, histogram-based metrics, Earth Mover's Distance, or other means of determining whether two features represent the same keypoints and / or elements of the game environment represented by map 202 and / or minimap 204. In various embodiments, each feature of the feature set of minimap 204 is compared with each feature of the feature set of map 202. In other words, in some embodiments, all permutations and combinations of feature sets are compared to determine matching features between feature sets. In one example, a forced comparison of all features is performed by calculating at least the distances between all features of the feature set extracted from minimap 204 and all features of the feature set extracted from map 202. In other embodiments, a portion or subset of the feature sets is compared. In one example, these features are matched until a threshold number of features are determined to be a match. Figure 2As shown, the matching features are represented by the solid line between minimap 204 and map 202. As described in this disclosure, two features can be considered a match if the distance between the two matrices representing two features is below a threshold and / or within a certain range. In one example, the threshold is 0.75.

[0075] Furthermore, in various embodiments, the computer system determines a certain number of features that match between minimap 204 and map 202 to determine the corresponding locations represented by minimap 204 and map 202. For example, such as Figure 2 As shown, minimap 204 represents a portion of map 202 indicated by solid lines, showing matching features extracted from minimap 204 that match features extracted from map 202. In an embodiment, a homography matrix is ​​computed once a sufficient number of matching features are identified (e.g., the subset of matching features between the feature set extracted from minimap 204 and the feature set extracted from map 202 exceeds a threshold).

[0076] In various embodiments, a transformation (minimap point to map point) matrix (e.g., homography matrix) is computed at least in part based on matching features extracted from minimap 204 and map 202. In one example, the transformation matrix represents global motion. In embodiments, minimap 204 and map 202 represent two distinct planes within the 3D space of the game environment, and the transformation matrix (e.g., homologous) includes parameters that project coordinates from the first plane to the second plane. In one example, the parameters include X, Y scaling factors, X, Y translational motion, and Z rotation. In various embodiments, the subset of features matched between features extracted from minimap 204 and map 202 includes noisy feature matching, and the matched subset of features may not correspond to a single transformation. In such embodiments, the computation of the transformation matrix minimizes backprojection error.

[0077] In various embodiments, minimap 204 represents the player's current position, and thus the center of minimap 204 represents the player's current position. In such an embodiment, the player's position on map 202 is represented by the coordinates of the minimap center, based at least in part on the following equation transformation:

[0078]

[0079] Where S i The coordinates represent the player's global position (e.g., the player's position on map 202), and H represents the homography matrix. In various embodiments, to determine positional information relative to map 202 (e.g., the player's position on map 202), the matrix representing the current position within minimap 204 is multiplied by H.

[0080] Figure 3An environment 300 for tracking motion, at least in part based on a set of minimaps, is illustrated according to at least one embodiment. In various embodiments, minimaps 304A-304D represent a set of minimaps acquired from the aforementioned gaming session. In one example, minimaps 304A-304D are acquired from a set of frames captured from the gaming session. In various embodiments, minimaps 304A-304D represent a series of consecutive frames. In other embodiments, minimaps 304A-304D are acquired from non-consecutive frames acquired from the gaming session.

[0081] In various embodiments, from minimaps (e.g., minimap 304A) and maps (e.g., those mentioned above) Figure 1 The features extracted from map 102 (described in the relevant description) may not have a sufficient number of matches, resulting in location information being determined at least in part based on one or more previously extracted minimaps (e.g., minimaps 304B-304D). In one example, a corner detection algorithm (e.g., Harris corner detection) can be used to identify features shared between minimaps 304A and 304B-304D. Furthermore, in embodiments, location information is determined at least in part based on optical flow (e.g., movement) between minimaps (e.g., minimaps 304A and 304B-304D) and previous locations. For example, at time N, the player's position is determined using the transformation matrix described above; at time N+1, the number of matching features between minimap 304A and the map is insufficient, resulting in location information (e.g., the player's position at time N+1) being determined by at least determining the player's movement between minimap 304A and at least one previously captured minimap (e.g., minimaps 304B-304D).

[0082] In various embodiments, optical flow algorithms (e.g., sparse and / or dense Lucas-Kanade (LK) algorithms) are used to determine positional information. In one example, the optical flow algorithm maps pixels in frames N and N+1 to determine movement between frames (e.g., portions of frames containing minimaps). In different embodiments, optical flow algorithms are used to determine horizontal movement of pixels and / or patches, as well as movement of the overall scene. In one embodiment, a sparse LK algorithm is used to determine the transformation matrix between two frames and / or minimaps. Furthermore, in various embodiments, noise is removed from the minimap, and the optical flow algorithm is used to perform positional tracking. In one example, since the minimap (e.g., minimap 304A) is a two-dimensional top-down view representing a three-dimensional scene, positional changes are proportional to changes in the corresponding X and Y directions on minimap 304A.

[0083] Figure 4An environment 400 for determining location information based at least in part on local location estimation, according to at least one embodiment, is illustrated. As described above, in various embodiments, location information is derived from a minimap (e.g., the one described above). Figure 1 Related minimap 104) and maps (e.g., above with Figure 1 There may not be a sufficient number of matches between the features extracted from the relevant map 102. As a result, location information is determined at least in part based on local location estimates, which are determined at least in part based on one or more previously extracted minimaps.

[0084] In various embodiments, one or more feature extraction algorithms (e.g., SIFT as described above) are used to extract features from minimaps (e.g., minimaps 404A and 404B). In one example, minimaps 404A and 404B are extracted from consecutive frames obtained from a game session as described above. Furthermore, once features have been extracted from minimaps 404A and 404B (in... Figure 4 The circle shown (indicated by a line connecting matching features) can be used to match features to determine a local location estimate, as described above. In various embodiments, determining location information based at least in part on a previous minimap (e.g., minimap 404B) includes calculating a transformation matrix (e.g., homology) between the current minimap (e.g., minimap 404A) and a previous minimap (e.g., minimap 404B). In embodiments, the previously acquired minimap and the features extracted from the previously acquired minimap are maintained by a computer system (e.g., a game server).

[0085] In embodiments, features extracted from minimaps 404A and 404B are compared to determine the matching feature sets between minimaps 404A and 404B. Furthermore, in various embodiments, the spatial relationships of keypoints extracted from minimaps 404A and 404B are used to filter features. Additionally, in such embodiments, distance functions and / or metrics as described above are used to determine whether a particular pair of features matches. Although in Figure 4 Only one previous minimap is shown, but a set of previous minimaps can be used in conjunction with various embodiments described in this disclosure to determine a set of homography maps to be applied to a set of previous minimaps, thereby determining location information (e.g., global motion estimation relative to the map). In one example, the current location information (e.g., the player's position on the map when minimap 404A is extracted) is determined by at least multiplying the homography matrix of the previous minimaps by the transformed coordinates of the current local position transformed to the previous minimaps.

[0086] As described above, a certain number of matches (e.g., ten pairs of features with a certain distance range) are determined before calculating location information; however, in various embodiments, this threshold can be dynamically modified (e.g., reducing the requirement of ten pairs to five pairs). For example, as described above, when comparing features between the current minimap and previous minimaps. Furthermore, in such embodiments, additional information is used to verify the location information when the threshold is modified. In one example, the location information varies at least partially based on the player's speed information, thereby ensuring that the location information matches the location determined at least partially based on the speed information within tolerance. In various embodiments, if neither the global motion estimation (e.g., location information determined using process 600 described in more detail below) nor the local motion homology estimation (e.g., location information determined at least partially based on the current minimap and one or more previous minimaps) includes a minimum number of matching features, the threshold associated with the distance metric (e.g., Euclidean distance) is gradually relaxed to include more local features (e.g., features that maintain relative distances in the minimap used for feature extraction). As described above, in this embodiment where the threshold is modified (e.g., relaxed and / or reduced), the location information is used at least in part based on an estimate consistent with current velocity information (e.g., avatar velocity in the game environment). In this way, location information can be obtained even if a sufficient number of matches are identified, for example, due to low-quality video frames or other noisy data.

[0087] Figure 5 An environment 500 for determining location information, at least in part, based on a transformation matrix, is illustrated according to at least one embodiment. As described above, in an embodiment, a map 502 representing the game environment is acquired, and a first feature set is extracted from the map 502 using one or more feature extraction algorithms. Furthermore, as described above, in an embodiment, a minimap 504 representing the location of the map 502 is acquired at a time point N. Additionally, according to at least one embodiment, a second feature set 508 is extracted from the minimap 504 using one or more feature extraction algorithms. In various embodiments, a distance function and / or a distance metric is used to compare the first feature set and the second feature set. In such embodiments, if the distance value is below a threshold, paired features (e.g., features from the first feature set and features from the second feature set) are considered a match.

[0088] As described above, once a sufficient number of features are matched (e.g., 10 pairs of features), a transformation matrix is ​​computed to transform the local location (e.g., the location within minimap 504) 506 into a global location (e.g., the location within map 503). In other embodiments, additional checks are performed to determine if the location information is accurate. In one example, the location information is compared to map 502 to determine if the location information includes locations outside a set of boundaries associated with map 502. In another example, as described above, the location information is compared to previous location information (e.g., the location associated with the last frame captured from the game session) to determine if the location information matches the player's most recent speed within tolerance. In various embodiments, if the checks indicate that the location information is inaccurate, local motion estimation is performed (e.g., as described above). Figure 4 (The relevant description) is used to update or otherwise modify location information.

[0089] Figure 6 This is a process 600 for determining location information in a game environment according to at least one embodiment. (See reference...) Figure 6 It should be understood that the arrangements and other arrangements described herein are merely illustrative. Other arrangements and elements (e.g., machines, interfaces, functions, commands, functional groups, etc.) may be used in addition to or in place of the arrangements and elements shown, and some elements may be executed in parallel or omitted entirely. Furthermore, many of the elements described herein are functional entities that can be implemented as discrete or distributed components or combined with other components, and implemented in any suitable combination and location. The various functions described herein as being performed by entities can be performed by hardware, firmware, and / or software. For example, various functions can be performed by a processor executing instructions stored in memory. Furthermore, Figure 6 The various functions shown can be executed in various orders (e.g., serial or parallel) or can be omitted entirely.

[0090] Now for reference Figure 6 Each block of the method 600 described herein includes a computational process that can be performed using any combination of hardware, firmware, and / or software. For example, various functions can be performed by a processor executing instructions stored in memory. The method can also be embodied as computer-usable instructions stored on a computer storage medium. These methods can be provided by standalone applications, services, or managed services (independently or in combination with another managed service) or plug-ins to another product, to name a few. Furthermore, by way of example, regarding Figure 7 Game server 702 describes method 600. However, these methods may be additionally or alternatively performed by any system or any combination of systems, including but not limited to those described herein.

[0091] In an embodiment, at block 602, the system executing process 600 acquires a map used during a game session. As described above, in an embodiment, the game session includes icons or other information indicating a map that will be used for a specific round or portion of the game session. In such an embodiment, the system executing process 600 acquires the map based at least in part on the information indicating the map. In one example, the map is acquired from one or more images of a captured game session. In another example, the map is acquired from a storage location and / or previously acquired. In an embodiment, at block 604, the system executing process 600 performs feature extraction on the map. In one example, a feature set is extracted from the map using one or more feature extraction algorithms as described above. In other embodiments, the feature set has been previously extracted (e.g., from a previous game session), and as a result, the feature set is acquired from a storage location instead of being extracted from the map.

[0092] In an embodiment, at block 606, the system executing process 600 acquires a minimap. In one example, a game session includes a video of the game being played, where the locations of the images include a minimap indicating the player's position. In different embodiments, the minimap is extracted from each frame of the game video, and process 600 is performed on each extracted minimap. In other embodiments, only a subset of the frames of the game video is processed (e.g., every other frame). In an embodiment, at block 608, the system executing process 600 performs feature extraction on the minimap. In one example, a feature set is extracted from the minimap using one or more feature extraction algorithms as described above.

[0093] In an embodiment, at block 610, the system performing process 600 compares features extracted from the map with features extracted from the minimap. In one example, as described above, a set of distance values ​​is calculated based at least in part on the features extracted from the map and the features extracted from the minimap. In various embodiments, all feature combinations are compared to determine a set of matching features between the map and the minimap. In an embodiment, at block 612, the system performing process 600 determines whether a good match has been determined. For example, a good match is determined if the set of matching features between the map and the minimap exceeds a threshold. If a good match is determined, the system performing process 600 continues to block 616 to determine the transformation matrix. For example, the system performing process 600 computes the homography matrix as described above.

[0094] However, if no good match is determined, at box 614, the system performing process 600 determines a local motion estimate according to the embodiment. For example, the transformation matrix can be determined at least in part based on a feature set extracted from a previous minimap, such as those described above. Figure 4The relevant description. In an embodiment, at block 610, once the system executing process 600 has determined the transformation matrix, it determines the player's position within the map. For example, the player's position is determined by multiplying the homography matrix by at least the player's current position within the minimap (e.g., the center of the minimap).

[0095] Now for reference Figure 7 , Figure 7 An environment 700 is illustrated, in which a game streaming system streams a game to a client device 704 according to some embodiments of the present disclosure. In embodiments, a game server 702 includes components, features, and / or functions similar to those of a game server, a client device 704 includes components, features, and / or functions similar to those of a client device described in the present disclosure, and a network 706 includes components, features, and / or functions similar to those of a network.

[0096] In at least one embodiment, game server 702 hosts a cloud computing platform used by client device 704. For example, a game session presented by a game application using client device 704 is facilitated by the cloud computing platform. In various embodiments, the cloud computing platform generates renderings (e.g., video data) of the game session presented on display 724. For example, the cloud computing platform dynamically provisiones and provides fully and / or partially virtualized computing environments. As an example, the cloud computing platform executes a game hosted on the cloud computing platform on one or more virtual machines.

[0097] In an embodiment, for a game session, client device 704 receives input data in response to input from an input device, transmits the input data to game server 702, receives encoded display data from game server 702, and displays the display data on display 724. Therefore, according to at least one embodiment, more computationally intensive computation and processing are offloaded to game server 702 (e.g., rendering of the game session is performed by the GPU of game server 702). In other words, the game session is streamed from game server 702 to client device 704, thereby reducing the graphics processing and rendering requirements of client device 704.

[0098] For example, regarding the instantiation of a game session, client device 704 displays frames of the game session on display 724, at least in part, based on display data received from game server 702. In various embodiments, client device 704 receives input to one of the input devices and generates input data in response. In such embodiments, client device 704 then transmits the input data to game server 702 via communication interface 720 and via network 706 (e.g., the Internet), and game server 702 receives the input data via communication interface 718. In embodiments, CPU receives the input data, processes the input data, and transmits the data to GPU, causing GPU to generate a rendering of the game session. For example, the input data is a representation of the user's character's movement in the game, such as firing a weapon, reloading, passing a ball, turning a vehicle, etc. In embodiments, rendering component 712 renders the game session (e.g., a result representing the input data), and rendering capture component 714 captures the rendering of the game session as display data (e.g., image data capturing the rendered frames of the game session). Furthermore, in various embodiments, the encoder 716 then encodes the display data to generate encoded display data, and the encoded display data is transmitted to the client device 704 over the network 706 via the communication interface 718. In such an embodiment, the client device 704 receives the encoded display data via the communication interface 720, and the decoder 722 decodes the encoded display data to generate display data. For example, after the display data is decoded, the client device 704 displays the display data via the display 724.

[0099] Furthermore, according to at least one embodiment, at least a portion of the game data capture device may reside on the client device 704 and be used to provide input data and / or input event data to the game server 702. In one example, the CPU 708 receives input data, processes the input data, and / or uses a soft analyzer and / or a hard analyzer to predict the occurrence of an event and / or confirm that the event occurred during a game session. In at least one embodiment, the client device 704 processes the input data and / or uses a soft analyzer (which may include a duration determiner and / or an interest determiner) to identify and / or predict events, event durations, event types, and / or time intervals preceding the event.

[0100] Reasoning and training logic

[0101] Figure 8A Inference and / or training logic 815 is shown for performing inference and / or training operations associated with one or more embodiments. The following is in conjunction with... Figure 8A and / or Figure 8B Provide details about reasoning and / or training logic 815.

[0102] In at least one embodiment, inference and / or training logic 815 may include, but is not limited to, code and / or data storage 801 for storing forward and / or output weights and / or input / output data, and / or other parameters for configuring neurons or layers of a neural network trained and / or used for inference in one or more embodiments. In at least one embodiment, training logic 815 may include or be coupled to code and / or data storage 801 for storing graph code or other software to control timing and / or sequence, wherein weight and / or other parameter information is loaded to configure logic including integer and / or floating-point units (collectively, arithmetic logic units (ALUs)). In at least one embodiment, code (such as graph code) loads weight or other parameter information into the processor ALU based on the architecture of the neural network to which the code corresponds. In at least one embodiment, code and / or data storage 801 stores weight parameters and / or input / output data of each layer of a neural network trained or used in one or more embodiments during forward propagation of input / output data and / or weight parameters using aspects of training and / or inference in one or more embodiments. In at least one embodiment, any portion of the code and / or data storage 801 may be included within other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory.

[0103] In at least one embodiment, any portion of the code and / or data storage 801 may be internal or external to one or more processors or other hardware logic devices or circuits. In at least one embodiment, the code and / or data storage 801 may be a cache memory, dynamic random access memory (“DRAM”), static random access memory (“SRAM”), non-volatile memory (e.g., flash memory), or other storage. In at least one embodiment, the choice of whether the code and / or data storage 801 is internal or external to the processor, for example, or including DRAM, SRAM, flash memory, or some other storage type, may depend on the available on-chip versus off-chip storage, the latency requirements of the training and / or inference functions being performed, the batch size of the data used in the inference and / or training of the neural network, or some combination of these factors.

[0104] In at least one embodiment, the inference and / or training logic 815 may include, but is not limited to, code and / or data storage 805 for storing backpropagation and / or output weights and / or input / output data corresponding to neurons or layers of a neural network trained and / or used for inference in one or more aspects of the embodiments. In at least one embodiment, during training and / or inference using one or more aspects of the embodiments, the code and / or data storage 805 stores weight parameters and / or input / output data for each layer of the neural network trained or used in one or more embodiments during backpropagation of input / output data and / or weight parameters. In at least one embodiment, the training logic 815 may include or be coupled to code and / or data storage 805 for storing graph code or other software to control timing and / or sequence, wherein weight and / or other parameter information is loaded to configure logic including integer and / or floating-point units (collectively, an arithmetic logic unit (ALU)).

[0105] In at least one embodiment, code (such as graph code) causes the architecture of the neural network corresponding to that code to load weights or other parameter information into the processor ALU. In at least one embodiment, any portion of the code and / or data storage 805 may be included together with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory. In at least one embodiment, any portion of the code and / or data storage 805 may be internal or external to one or more processors or other hardware logic devices or circuits. In at least one embodiment, the code and / or data storage 805 may be cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory), or other storage. In at least one embodiment, the choice of whether the code and / or data storage 805 is internal or external to the processor, for example, including DRAM, SRAM, flash memory, or some other type of storage, may depend on the available on-chip versus off-chip storage, the latency requirements of the training and / or inference functions being performed, the batch size of the data used in the inference and / or training of the neural network, or some combination of these factors.

[0106] In at least one embodiment, code and / or data storage 801 and code and / or data storage 805 may be separate storage structures. In at least one embodiment, code and / or data storage 801 and code and / or data storage 805 may be the same storage structure. In at least one embodiment, code and / or data storage 801 and code and / or data storage 805 may be partially combined and partially separated. In at least one embodiment, any portion of code and / or data storage 801 and code and / or data storage 805 may be included together with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory.

[0107] In at least one embodiment, the inference and / or training logic 815 may include, but is not limited to, one or more arithmetic logic units (“ALUs”) 810 (including integer and / or floating-point units) for performing logical and / or mathematical operations at least in part based on or instructed by training and / or inference code (e.g., graph code), the results of which may produce activations (e.g., output values ​​from layers or neurons within a neural network) stored in activation storage 820, which are functions of input / output and / or weight parameter data stored in code and / or data storage 801 and / or code and / or data storage 805. In at least one embodiment, activations stored in activation storage 820 are generated based on linear algebra and / or matrix-based mathematics performed by ALU 810 in response to execution instructions or other code, wherein weight values ​​stored in code and / or data storage 805 and / or code and / or data storage 801 are used as operands, and other values, such as bias values, gradient information, momentum values, or other parameters or hyperparameters, may be stored in code and / or data storage 805 or code and / or data storage 801 or other on-chip or off-chip storage.

[0108] In at least one embodiment, one or more processors or other hardware logic devices or circuits include one or more ALUs 810, while in another embodiment, one or more ALUs 810 may be external to the processor or other hardware logic device or the circuitry that uses them (e.g., a coprocessor). In at least one embodiment, ALUs 810 may be included within an execution unit of a processor, or otherwise included in an ALU bank accessible by the execution unit of the processor, which may be within the same processor or distributed among different types of processors (e.g., a central processing unit, a graphics processing unit, a fixed-function unit, etc.). In at least one embodiment, code and / or data storage 801, code and / or data storage 805, and activation storage 820 may share a processor or other hardware logic device or circuitry, while in another embodiment, they may be in different processors or other hardware logic devices or circuitry, or in some combination of the same and different processors or other hardware logic devices or circuitry. In at least one embodiment, any portion of activation storage 820 may be included together with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory. Furthermore, inference and / or training code may be stored together with other code accessible to the processor or other hardware logic or circuitry, and may be retrieved and / or processed using the processor’s fetch, decode, schedule, execute, exit, and / or other logic circuitry.

[0109] In at least one embodiment, the active memory 820 may be a cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory), or other memory. In at least one embodiment, the active memory 820 may be entirely or partially located within or outside one or more processors or other logic circuits. In at least one embodiment, the choice of whether the active memory 820 is internal to or external to the processor, for example, or including DRAM, SRAM, flash memory, or certain other memory types, may depend on the availability of on-chip versus off-chip memory, latency requirements for performing training and / or inference functions, batch size of data used in inference and / or training of neural networks, or some combination of these factors.

[0110] In at least one embodiment, Figure 8A The inference and / or training logic 815 shown can be used in conjunction with an application-specific integrated circuit (“ASIC”), such as those from Google. Processing unit, from Graphcore TM The inference processing unit (IPU) or from Intel. (e.g., "Lake Crest") processor. In at least one embodiment, Figure 8AThe inference and / or training logic 815 shown can be used in conjunction with central processing unit (“CPU”) hardware, graphics processing unit (“GPU”) hardware or other hardware (such as field programmable gate array (“FPGA”)).

[0111] Figure 8B Inference and / or training logic 815 according to at least one embodiment is illustrated. In at least one embodiment, the inference and / or training logic 815 may include, but is not limited to, hardware logic, wherein computational resources are dedicated or otherwise exclusively used together with weight values ​​or other information corresponding to one or more layers of neurons within a neural network. In at least one embodiment, Figure 8B The inference and / or training logic 815 shown can be used in conjunction with an application-specific integrated circuit (ASIC), such as those from Google. Processing unit, from Graphcore TM The inference processing unit (IPU) or from Intel. (e.g., "Lake Crest") processor. In at least one embodiment, Figure 8B The inference and / or training logic 815 shown can be used in conjunction with central processing unit (CPU) hardware, graphics processing unit (GPU) hardware, or other hardware (e.g., field-programmable gate array (FPGA)). In at least one embodiment, the inference and / or training logic 815 includes, but is not limited to, code and / or data storage 801 and code and / or data storage 805, which can be used to store code (e.g., graph code), weight values, and / or other information, including bias values, gradient information, momentum values, and / or other parameter or hyperparameter information. Figure 8B In at least one embodiment shown, each of code and / or data storage 801 and code and / or data storage 805 is associated with dedicated computing resources (e.g., computing hardware 802 and computing hardware 806), respectively. In at least one embodiment, each of computing hardware 802 and computing hardware 806 includes one or more ALUs that perform mathematical functions (e.g., linear algebraic functions) on the information stored in code and / or data storage 801 and code and / or data storage 805, respectively, with the results stored in active storage 820.

[0112] In at least one embodiment, each of the code and / or data storage 801 and 805 and the corresponding computing hardware 802 and 806 corresponds to a different layer of the neural network, such that an activation obtained from one storage / computation pair 801 / 802 of the code and / or data storage 801 and computing hardware 802 is provided as input to the next storage / computation pair 805 / 806 of the code and / or data storage 805 and computing hardware 806, in order to reflect the conceptual organization of the neural network. In at least one embodiment, each storage / computation pair 801 / 802 and 805 / 806 may correspond to more than one neural network layer. In at least one embodiment, additional storage / computation pairs (not shown) may be included in the inference and / or training logic 815 after or in parallel with the storage / computation pairs 801 / 802 and 805 / 806.

[0113] Neural network training and deployment

[0114] Figure 9 Training and deployment of a deep neural network according to at least one embodiment are illustrated. In at least one embodiment, an untrained neural network 906 is trained using a training dataset 902. In at least one embodiment, the training framework 904 is the PyTorch framework, while in other embodiments, the training framework 904 is TensorFlow, Boost, Caffe, Microsoft Cognitive Toolkit / CNTK, MXNet, Chainer, Keras, Deeplearning4j, or other training frameworks. In at least one embodiment, the training framework 904 trains the untrained neural network 906 and enables it to be trained using the processing resources described herein to generate a trained neural network 908. In at least one embodiment, the weights can be randomly selected or selected by pre-training using a deep belief network. In at least one embodiment, training can be performed in a supervised, partially supervised, or unsupervised manner.

[0115] In at least one embodiment, supervised learning is used to train an untrained neural network 906, wherein the training dataset 902 includes inputs paired with desired outputs for input, or wherein the training dataset 902 includes inputs with known outputs and the outputs of the neural network 906 are manually graded. In at least one embodiment, the untrained neural network 906 is trained in a supervised manner and processes inputs from the training dataset 902, comparing the resulting outputs with a set of expected or desired outputs. In at least one embodiment, the error is then backpropagated through the untrained neural network 906. In at least one embodiment, a training framework 904 adjusts the weights controlling the untrained neural network 906. In at least one embodiment, the training framework 904 includes tools for monitoring the degree to which the untrained neural network 906 converges to a model (such as a trained neural network 908) suitable for generating correct answers (such as results 914) based on input data (such as a new dataset 912). In at least one embodiment, the training framework 904 repeatedly trains the untrained neural network 906 while adjusting the weights to refine the output of the untrained neural network 906 using a loss function and tuning algorithms such as stochastic gradient descent. In at least one embodiment, the training framework 904 trains the untrained neural network 906 until the untrained neural network 906 reaches the desired accuracy. In at least one embodiment, the trained neural network 908 can then be deployed to implement any number of machine learning operations.

[0116] In at least one embodiment, unsupervised learning is used to train an untrained neural network 906, wherein the untrained neural network 906 attempts to train itself using unlabeled data. In at least one embodiment, the unsupervised learning training dataset 902 will include input data without any associated output data or "ground truth" data. In at least one embodiment, the untrained neural network 906 can learn groupings within the training dataset 902 and can determine how each input relates to the untrained dataset 902. In at least one embodiment, unsupervised training can be used to generate a self-organizing graph in a trained neural network 908, which is capable of performing operations useful for reducing the dimensionality of a new dataset 912. In at least one embodiment, unsupervised training can also be used to perform anomaly detection, which allows the identification of data points in the new dataset 912 that deviate from the normal patterns of the new dataset 912.

[0117] In at least one embodiment, semi-supervised learning can be used, which is a technique in which a mixture of labeled and unlabeled data is included in the training dataset 902. In at least one embodiment, the training framework 904 can be used to perform incremental learning, such as through transfer learning techniques. In at least one embodiment, incremental learning enables the trained neural network 908 to adapt to a new dataset 912 without forgetting the knowledge injected into the trained neural network 908 during initial training.

[0118] In at least one embodiment, the training framework 904 is a framework that incorporates a software development kit such as the OpenVINO (Open Visual Inference and Neural Network Optimization) toolkit. In at least one embodiment, the OpenVINO toolkit is, for example, a toolkit developed by Intel Corporation of Santa Clara, California.

[0119] In at least one embodiment, OpenVINO is a toolkit for facilitating the development of applications (particularly neural network applications) for various tasks and operations, such as human visual simulation, speech recognition, natural language processing, recommender systems, and / or variations thereof. In at least one embodiment, OpenVINO supports neural networks, such as convolutional neural networks (CNNs), recurrent neural networks, and / or attention-based neural networks, and / or various other neural network models. In at least one embodiment, OpenVINO supports various software libraries, such as OpenCV, OpenCL, and / or variations thereof.

[0120] In at least one embodiment, OpenVINO supports neural network models for a variety of tasks and operations, such as classification, segmentation, object detection, face recognition, speech recognition, pose estimation (e.g., people and / or objects), monocular depth estimation, image inpainting, style transfer, action recognition, colorization, and / or variations thereof.

[0121] In at least one embodiment, OpenVINO includes one or more software tools and / or modules for model optimization, also referred to as a model optimizer. In at least one embodiment, the model optimizer is a command-line tool that facilitates the transition between training and deployment of a neural network model. In at least one embodiment, the model optimizer optimizes the neural network model for execution on various devices and / or processing units such as GPUs, CPUs, PPUs, GPGPUs, and / or variants thereof. In at least one embodiment, the model optimizer generates an internal representation of the model and optimizes the model to generate an intermediate representation. In at least one embodiment, the model optimizer reduces the number of layers in the model. In at least one embodiment, the model optimizer removes layers from the model used for training. In at least one embodiment, the model optimizer performs various neural network operations, such as modifying the model's input (e.g., adjusting the size of the model's input), modifying the size of the model's input (e.g., modifying the model's batch size), modifying the model's structure (e.g., modifying the model's layers), normalizing, standardizing, quantizing (e.g., converting the model's weights from a first representation such as floating-point to a second representation such as integer), and / or variants thereof.

[0122] In at least one embodiment, OpenVINO includes one or more software libraries for inference, also referred to as an inference engine. In at least one embodiment, the inference engine is a C++ library or any suitable programming language library. In at least one embodiment, the inference engine is used to infer input data. In at least one embodiment, the inference engine implements various classes to infer input data and generate one or more results. In at least one embodiment, the inference engine implements one or more API functions to process intermediate representations, set input and / or output formats, and / or execute models on one or more devices.

[0123] In at least one embodiment, OpenVINO provides various capabilities for heterogeneous execution of one or more neural network models. In at least one embodiment, heterogeneous execution or heterogeneous computing refers to one or more computational processes and / or systems utilizing one or more types of processors and / or cores. In at least one embodiment, OpenVINO provides various software functions to execute programs on one or more devices. In at least one embodiment, OpenVINO provides various software functions to execute programs and / or portions of programs on different devices. In at least one embodiment, OpenVINO provides various software functions, for example, to run a first code portion on a CPU and a second code portion on a GPU and / or FPGA. In at least one embodiment, OpenVINO provides various software functions to execute one or more layers of a neural network on one or more devices (e.g., executing a first set of layers on a first device (e.g., a GPU) and a second set of layers on a second device (e.g., a CPU)).

[0124] In at least one embodiment, OpenVINO includes various functionalities similar to those associated with CUDA programming models, such as various neural network model operations associated with frameworks such as TensorFlow, PyTorch, and / or their variants. In at least one embodiment, one or more CUDA programming model operations are performed using OpenVINO. In at least one embodiment, the various systems, methods, and / or techniques described herein are implemented using OpenVINO.

[0125] Data Center

[0126] Figure 10 An example data center 1000 that can be used in at least one embodiment is shown. In at least one embodiment, the data center 1000 includes a data center infrastructure layer 1010, a framework layer 1020, a software layer 1030, and an application layer 1040.

[0127] In at least one embodiment, such as Figure 10As shown, the data center infrastructure layer 1010 may include a resource coordinator 1012, grouped computing resources 1014, and node computing resources (“nodes CR”) 1016(1)-1016(N), where “N” represents a positive integer (which may be an integer “N” different from the integers used in other diagrams). In at least one embodiment, nodes CR 1016(1)-1016(N) may include, but are not limited to, any number of central processing units (“CPUs”) or other processors (including accelerators, field-programmable gate arrays (FPGAs), graphics processors, etc.), memory storage devices 1018(1)-1018(N) (e.g., dynamic read-only memory, solid-state storage, or disk drives), network input / output (“NW I / O”) devices, network switches, virtual machines (“VMs”), power modules, and cooling modules, etc. In at least one embodiment, one or more nodes CR 1016(1)-1016(N) may be servers having one or more of the aforementioned computing resources.

[0128] In at least one embodiment, the grouped computing resources 1014 may include individual groups of node CRs housed within one or more racks (not shown), or a plurality of racks housed within data centers (also not shown) in various geographical locations. In at least one embodiment, the individual groups of node CRs within the grouped computing resources 1014 may include computing, networking, memory, or storage resources that can be configured or allocated to support groups of one or more workloads. In at least one embodiment, several node CRs, including CPUs or processors, may be grouped within one or more racks to provide computing resources to support one or more workloads. In at least one embodiment, one or more racks may also include any number of power modules, cooling modules, and network switches in any combination.

[0129] In at least one embodiment, resource coordinator 1012 may be configured or otherwise control one or more nodes CR1016(1)-1016(N) and / or grouped computing resources 1014. In at least one embodiment, resource coordinator 1012 may include a Software Design Infrastructure (“SDI”) management entity for data center 1000. In at least one embodiment, resource coordinator 1012 may include hardware, software, or some combination thereof.

[0130] In at least one embodiment, such as Figure 10As shown, framework layer 1020 includes a job scheduler 1022, a configuration manager 1024, a resource manager 1026, and a distributed file system 1028. In at least one embodiment, framework layer 1020 may include a framework of software 1032 supporting software layer 1030 and / or one or more applications 1042 supporting application layer 1040. In at least one embodiment, software 1032 or application 1042 may respectively include web-based service software or applications, such as service software or applications provided by Amazon Web Services, Google Cloud, and Microsoft Azure. In at least one embodiment, framework layer 1020 may be, but is not limited to, a type of free and open-source software web application framework, such as Apache Spark, which can leverage distributed file system 1028 for large-scale data processing (e.g., “big data”). TM (Hereinafter referred to as "Spark"). In at least one embodiment, the job scheduler 1022 may include a Spark driver for facilitating the scheduling of workloads supported by various layers of data center 1000. In at least one embodiment, the configuration manager 1024 may be able to configure different layers, such as software layer 1030 and framework layer 1020 including Spark and a distributed file system 1028 for supporting large-scale data processing. In at least one embodiment, the resource manager 1026 may be able to manage clustered or grouped computing resources mapped to or allocated to support the distributed file system 1028 and job scheduler 1022. In at least one embodiment, the clustered or grouped computing resources may include grouped computing resources 1014 at data center infrastructure layer 1010. In at least one embodiment, the resource manager 1026 may coordinate with resource coordinator 1012 to manage these mapped or allocated computing resources.

[0131] In at least one embodiment, the software 1032 included in the software layer 1030 may include software used by at least portions of nodes CR1016(1)-1016(N), grouped computing resources 1014, and / or the distributed file system 1028 of the framework layer 1020. In at least one embodiment, one or more types of software may include, but are not limited to, Internet web page search software, email virus scanning software, database software, and streaming video content software.

[0132] In at least one embodiment, one or more applications 1042 included in application layer 1040 may include one or more types of applications used by at least portions of nodes CR1016(1)-1016(N), grouped computing resources 1014, and / or the distributed file system 1028 of framework layer 1020. In at least one embodiment, one or more types of applications may include, but are not limited to, any number of genomics applications, cognitive computing, applications, and machine learning applications, including training or inference software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), or other machine learning applications used in conjunction with one or more embodiments.

[0133] In at least one embodiment, any of the configuration manager 1024, resource manager 1026, and resource coordinator 1012 can implement any number and type of self-modification actions based on any amount and type of data acquired in any technically feasible manner. In at least one embodiment, self-modification actions can mitigate potentially poor configuration decisions by data center operators of data center 1000 and can prevent underutilization and / or poor performance of the data center.

[0134] In at least one embodiment, data center 1000 may include tools, services, software, or other resources for training one or more machine learning models or using one or more machine learning models to predict or infer information according to one or more embodiments described herein. For example, in at least one embodiment, a machine learning model can be trained by calculating weight parameters based on a neural network architecture using the software and computing resources described above with respect to data center 1000. In at least one embodiment, information can be inferred or predicted using trained machine learning models corresponding to one or more neural networks using the resources described above with respect to data center 1000 by using weight parameters calculated through one or more training techniques described herein.

[0135] In at least one embodiment, the data center may use a CPU, application-specific integrated circuit (ASIC), GPU, FPGA, or other hardware to utilize the aforementioned resources to perform training and / or inference. Furthermore, one or more of the aforementioned software and / or hardware resources may be configured as a service to allow a user to train or perform information inference, such as image recognition, speech recognition, or other artificial intelligence services.

[0136] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8BDetails are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 can be... Figure 10 It is used in systems for inference or prediction operations based at least in part on weight parameters calculated using the neural network training operations, neural network functions and / or architectures, or neural network use cases described herein.

[0137] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0138] Autonomous vehicles

[0139] Figure 11A Examples of autonomous vehicles 1100 according to at least one embodiment are shown. In at least one embodiment, the autonomous vehicle 1100 (which may alternatively be referred to herein as "vehicle 1100") may be, but is not limited to, a passenger vehicle, such as a car, truck, bus, and / or another type of vehicle accommodating one or more passengers. In at least one embodiment, vehicle 1100 may be a semi-tractor-trailer truck for hauling goods. In at least one embodiment, vehicle 1100 may be an aircraft, a robotic vehicle, or other type of vehicle.

[0140] Autonomous vehicles can be described according to the levels of automation defined by the National Highway Traffic Safety Administration (“NHTSA”) and the Society of Automotive Engineers (“SAE”) of the U.S. Department of Transportation in their “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles” (e.g., standard number J3016-201806, published June 15, 2018; standard number J3016-201609, published September 30, 2016; and previous and future versions of that standard). In at least one embodiment, vehicle 1100 may be able to have one or more functions according to levels 1 through 5 of autonomous driving. For example, in at least one embodiment, vehicle 1100 may be able to have conditional automation (level 3), high automation (level 4), and / or full automation (level 5), depending on the embodiment.

[0141] In at least one embodiment, vehicle 1100 may include, but is not limited to, components such as chassis, body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other vehicle components. In at least one embodiment, vehicle 1100 may include, but is not limited to, propulsion system 1150, such as an internal combustion engine, a hybrid powertrain, an all-electric motor, and / or another type of propulsion system. In at least one embodiment, propulsion system 1150 may be connected to the drivetrain of vehicle 1100, which may include, but is not limited to, a transmission, for enabling propulsion of vehicle 1100. In at least one embodiment, propulsion system 1150 may be controlled in response to receiving a signal from throttle / accelerator 1152.

[0142] In at least one embodiment, when the propulsion system 1150 is operating (e.g., when the vehicle 1100 is in motion), the steering system 1154 (which may include, but is not limited to, a steering wheel) is used to steer the vehicle 1100 (e.g., along a desired path or route). In at least one embodiment, the steering system 1154 may receive signals from the steering actuator 1156. In at least one embodiment, for fully automated (Level 5) functionality, the steering wheel may be optional. In at least one embodiment, the brake sensor system 1146 may be used to operate the vehicle brakes in response to signals received from the brake actuator 1148 and / or brake sensors.

[0143] In at least one embodiment, one or more controllers 1136 may include, but are not limited to, one or more system-on-a-chip (“SoC”). Figure 11A(not shown) and / or a graphics processing unit (“GPU”) provides signals (e.g., representing commands) to one or more components and / or systems of vehicle 1100. For example, in at least one embodiment, one or more controllers 1136 may send signals to operate vehicle braking via brake actuator 1148, to operate steering system 1154 via one or more steering actuators 1156, and to operate propulsion system 1150 via one or more throttle / accelerators 1152. In at least one embodiment, one or more controllers 1136 may include one or more on-board (e.g., integrated) computing devices that process sensor signals and output operating commands (e.g., signals representing commands) to enable autonomous driving and / or assist a human driver in driving vehicle 1100. In at least one embodiment, one or more controllers 1136 may include a first controller for autonomous driving functions, a second controller for functional safety functions, a third controller for artificial intelligence functions (e.g., computer vision), a fourth controller for infotainment functions, a fifth controller for redundancy in emergency situations, and / or other controllers. In at least one embodiment, a single controller may handle two or more of the functions described above, and two or more controllers may handle a single function and / or any combination thereof.

[0144] In at least one embodiment, one or more controllers 1136 provide signals for controlling one or more components and / or systems of vehicle 1100 in response to sensor data received from one or more sensors (e.g., sensor inputs). In at least one embodiment, the sensor data can be received from, for example, but not limited to, the following sensors: one or more Global Navigation Satellite System (“GNSS”) sensors 1158 (e.g., one or more Global Positioning System sensors), one or more RADAR sensors 1160, one or more ultrasonic sensors 1162, one or more LIDAR sensors 1164, one or more inertial measurement unit (IMU) sensors 1166 (e.g., one or more accelerometers, one or more gyroscopes, one or more magnetic compasses, one or more magnetometers, etc.), one or more microphones 1196, one or more stereo cameras 1168, one or more wide-angle cameras 1170 (e.g., fisheye cameras), one or more infrared cameras 1172, one or more surround cameras 1174 (e.g., 360-degree cameras), remote cameras ( Figure 11A (not shown in the image), medium-range camera ( Figure 11A(not shown), one or more speed sensors 1144 (e.g., for measuring the speed of vehicle 1100), one or more vibration sensors 1142, one or more steering sensors 1140, one or more brake sensors (e.g., as part of brake sensor system 1146) and / or other sensor types.

[0145] In at least one embodiment, one or more controllers 1136 may receive input (e.g., represented by input data) from the dashboard 1132 of the vehicle 1100 and provide output (e.g., represented by output data, display data, etc.) via a human-machine interface (“HMI”) display 1134, a voice signaler, a speaker, and / or via other components of the vehicle 1100. In at least one embodiment, the output may include information such as vehicle speed, velocity, time, map data (e.g., high-definition map). Figure 11A The HMI display 1134 may display information such as (not shown), location data (e.g., the location of vehicle 1100, for example on a map), direction, the location of other vehicles (e.g., occupying a grid), information about objects, and the state of objects perceived by one or more controllers 1136. For example, in at least one embodiment, the HMI display 1134 may display information about the presence of one or more objects (e.g., road signs, warning signs, traffic light changes, etc.) and / or information about driving maneuvers that have been, are being, or will be made (e.g., changing lanes now, reaching exit 34B within two miles, etc.).

[0146] In at least one embodiment, vehicle 1100 further includes a network interface 1124 that can communicate over one or more networks using one or more wireless antennas 1126 and / or one or more modems. For example, in at least one embodiment, network interface 1124 may be able to communicate over Long Term Evolution (“LTE”), Wideband Code Division Multiple Access (“WCDMA”), Universal Mobile Telecommunications System (“UMTS”), Global System for Mobile Communications (“GSM”), IMT-CDMA Multicarrier (“CDMA2000”) networks, etc. In at least one embodiment, one or more wireless antennas 1126 may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.) using one or more local area networks (such as Bluetooth, Bluetooth Low Energy (LE), Z-Wave, ZigBee, etc.) and / or one or more low-power wide area networks (“LPWAN”) (such as LoRaWAN, SigFox, etc. protocols).

[0147] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details regarding the inference and / or training logic 815 are provided. In at least one embodiment, the inference and / or training logic 815 can be... Figure 11A The system is used for inference or prediction operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures, or neural network use cases as described herein.

[0148] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0149] Figure 11B The illustration shows an embodiment according to at least one of the embodiments. Figure 11A Examples of camera positions and fields of view for the autonomous vehicle 1100. In at least one embodiment, the camera and its respective field of view are an example embodiment and are not intended to be limiting. For example, in at least one embodiment, additional and / or alternative cameras may be included and / or the cameras may be located at different positions on the vehicle 1100.

[0150] In at least one embodiment, the camera type used for the camera may include, but is not limited to, a digital camera suitable for use with components and / or systems of vehicle 1100. In at least one embodiment, one or more cameras may operate at Automotive Safety Integrity Level (“ASIL”) B and / or other ASILs. In at least one embodiment, the camera type may be capable of having any image capture rate, such as 60 frames per second (fps), 120 fps, 240 fps, etc., depending on the embodiment. In at least one embodiment, the camera may be capable of using a rolling shutter, a global shutter, other types of shutters, or combinations thereof. In at least one embodiment, the color filter array may include a red-to-clear-to-clear (“RCCC”) color filter array, a red-to-clear-to-blue (“RCCB”) color filter array, a red-blue-green (“RBGC”) color filter array, a Foveon X3 color filter array, a Bayer sensor (“RGGB”) color filter array, a monochrome sensor color filter array, and / or other types of color filter arrays. In at least one embodiment, a transparent pixel camera, such as a camera with an array of RCCC, RCCB and / or RBGC color filters, may be used to improve photosensitivity.

[0151] In at least one embodiment, one or more cameras may be used to perform advanced driver assistance system (“ADAS”) functions (e.g., as part of a redundancy or fail-safe design). For example, in at least one embodiment, a multi-function monocular camera may be installed to provide functions including lane departure warning, traffic sign assist, and intelligent headlight control. In at least one embodiment, one or more cameras (e.g., all cameras) may simultaneously record and provide image data (e.g., video).

[0152] In at least one embodiment, one or more cameras may be mounted in a mounting assembly, such as a custom-designed (3D-printed) assembly, to remove stray light and reflected light from within the vehicle 1100 (e.g., reflected light from the dashboard reflected in the windshield mirror), which may interfere with the camera's image data capture capabilities. Regarding the rearview mirror mounting assembly, in at least one embodiment, the rearview mirror assembly may be 3D-printed custom-made such that the camera mounting plate matches the shape of the rearview mirror. In at least one embodiment, one or more cameras may be integrated into the rearview mirror. In at least one embodiment, for side-view cameras, one or more cameras may also be integrated into four pillars at each corner of the cabin.

[0153] In at least one embodiment, a camera (e.g., a forward-facing camera) having a field of view including portions of the environment in front of the vehicle 1100 can be used for surround view to help identify the path and obstacles ahead, and to assist in providing information crucial for generating an occupancy grid and / or determining a preferred vehicle path, with the help of one or more controllers 1136 and / or control SoCs. In at least one embodiment, the forward-facing camera can be used to perform many ADAS functions similar to LIDAR, including but not limited to emergency braking, pedestrian detection, and collision avoidance. In at least one embodiment, the forward-facing camera can also be used for ADAS functions and systems, including but not limited to lane departure warning (“LDW”), adaptive cruise control (“ACC”), and / or other functions (such as traffic sign recognition).

[0154] In at least one embodiment, a variety of cameras can be used in a forward-facing configuration, including, for example, a monocular camera platform including a CMOS (“complementary metal-oxide-semiconductor”) color imager. In at least one embodiment, a wide-angle camera 1170 can be used to sense objects entering the view from the periphery (e.g., pedestrians, intersection traffic, or bicycles). Although in Figure 11BOnly one wide-angle camera 1170 is shown, but in other embodiments, the vehicle 1100 may have any number (including zero) of wide-angle cameras. In at least one embodiment, any number of remote cameras 1198 (e.g., a pair of telescopic stereo cameras) can be used for depth-based object detection, especially for objects for which a neural network has not yet been trained. In at least one embodiment, one or more remote cameras 1198 can also be used for object detection and classification, as well as basic object tracking.

[0155] In at least one embodiment, any number of stereo cameras 1168 may also be included in a forward configuration. In at least one embodiment, one or more stereo cameras 1168 may include an integrated control unit comprising a scalable processing unit that can provide programmable logic (“FPGA”) and a multi-core microprocessor with a controller area network (“CAN”) or Ethernet interface integrated on a single chip. In at least one embodiment, such a unit may be used to generate a 3D map of the environment of vehicle 1100, including distance estimates for all points in the image. In at least one embodiment, one or more stereo cameras 1168 may include, but are not limited to, a compact stereo vision sensor that may include, but is not limited to, two camera lenses (one on each side) and an image processing chip that can measure the distance from vehicle 1100 to a target object and use the generated information (e.g., metadata) to activate autonomous emergency braking and lane departure warning functions. In at least one embodiment, other types of stereo cameras 1168 may be used in addition to or instead of those described herein.

[0156] In at least one embodiment, a camera (e.g., a side-view camera) having a field of view including portions of the environment on the sides of vehicle 1100 can be used for surround view, providing information for creating and updating an occupied grid, and generating a side-impact collision warning. For example, in at least one embodiment, a surround camera 1174 (e.g., such as...) Figure 11B The four surround cameras shown can be positioned on vehicle 1100. In at least one embodiment, one or more surround cameras 1174 can include, but are not limited to, any number and combination of wide-angle cameras, one or more fisheye cameras, one or more 360-degree cameras, and / or similar cameras. For example, in at least one embodiment, four fisheye cameras can be located at the front, rear, and sides of vehicle 1100. In at least one embodiment, vehicle 1100 can use three surround cameras 1174 (e.g., left, right, and rear) and can utilize one or more other cameras (e.g., forward-facing cameras) as a fourth surround-view camera.

[0157] In at least one embodiment, a camera (e.g., a rear-view camera) having a field of view including portions of the environment behind the vehicle 1100 can be used for parking assistance, surround view, rear collision warning, and creating and updating occupancy grids. In at least one embodiment, a wide variety of cameras can be used, including but not limited to cameras also suitable as one or more forward-facing cameras (e.g., long-range camera 1198 and / or one or more mid-range cameras 1176, one or more stereo cameras 1168, one or more infrared cameras 1172, etc.), as described herein.

[0158] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details regarding the inference and / or training logic 815 are provided. In at least one embodiment, the inference and / or training logic 815 can be... Figure 11B It is used in systems for inference or prediction operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures, or neural network use cases as described herein.

[0159] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0160] Figure 11C It is shown that according to at least one embodiment Figure 11A A block diagram of an example system architecture for an autonomous vehicle 1100. In at least one embodiment, Figure 11C Each component, feature, and system of vehicle 1100 is shown connected via bus 1102. In at least one embodiment, bus 1102 may include, but is not limited to, a CAN data interface (which may alternatively be referred to herein as a "CAN bus"). In at least one embodiment, CAN may be a network within vehicle 1100 used to assist in the control of various features and functions of vehicle 1100, such as brake actuation, acceleration, braking, steering, windshield wipers, etc. In at least one embodiment, bus 1102 may be configured to have dozens or even hundreds of nodes, each node having its own unique identifier (e.g., a CAN ID). In at least one embodiment, bus 1102 may be read to locate steering wheel angle, ground speed, engine revolutions per minute ("RPM"), button positions, and / or other vehicle status indicators. In at least one embodiment, bus 1102 may be an ASIL B compliant CAN bus.

[0161] In at least one embodiment, FlexRay and / or Ethernet protocols may be used in addition to or instead of CAN. In at least one embodiment, any number of buses forming bus 1102 may be present, including but not limited to zero or more CAN buses, zero or more FlexRay buses, zero or more Ethernet buses, and / or zero or more other types of buses using different protocols. In at least one embodiment, two or more buses may be used to perform different functions and / or may be used for redundancy. For example, a first bus may be used for a collision avoidance function, and a second bus may be used for actuation control. In at least one embodiment, each bus in bus 1102 may communicate with any component of vehicle 1100, and two or more buses in bus 1102 may communicate with corresponding components. In at least one embodiment, each of any number of System-on-Chip (“SoC”) 1104 (e.g., SoC 1104(A) and SoC 1104(B)), each of one or more controllers 1136 and / or each computer within the vehicle can access the same input data (e.g., input from sensors of vehicle 1100) and can be connected to a common bus, such as a CAN bus.

[0162] In at least one embodiment, vehicle 1100 may include one or more controllers 1136, such as those described herein. Figure 11A As described above. In at least one embodiment, controller 1136 can be used for a wide variety of functions. In at least one embodiment, controller 1136 can be coupled to any of various other components and systems of vehicle 1100 and can be used to control vehicle 1100, artificial intelligence of vehicle 1100, infotainment and / or other functions of vehicle 1100.

[0163] In at least one embodiment, vehicle 1100 may include any number of SoCs 1104. In at least one embodiment, each of the SoCs 1104 may include, but is not limited to, a central processing unit (“one or more CPUs”) 1106, a graphics processing unit (“one or more GPUs”) 1108, one or more processors 1110, one or more caches 1112, one or more accelerators 1114, one or more data storage 1116, and / or other components and features not shown. In at least one embodiment, one or more SoCs 1104 may be used to control vehicle 1100 on a wide variety of platforms and systems. For example, in at least one embodiment, one or more SoCs 1104 may be combined with a high-definition (“HD”) map 1122 in a system (e.g., the system of vehicle 1100), which may be located from one or more servers via a network interface 1124. Figure 11C (Not shown in the image) Get map refresh and / or update.

[0164] In at least one embodiment, one or more CPUs 1106 may include CPU clusters or CPU complexes (which may alternatively be referred to herein as “CCPLEX”). In at least one embodiment, one or more CPUs 1106 may include multiple cores and / or a secondary (“L2”) cache. For example, in at least one embodiment, one or more CPUs 1106 may include eight cores in a coherent multiprocessor configuration. In at least one embodiment, one or more CPUs 1106 may include four dual-core clusters, each with a dedicated L2 cache (e.g., 2 megabytes (MB) L2 cache). In at least one embodiment, one or more CPUs 1106 (e.g., CCPLEX) may be configured to support simultaneous cluster operation, which allows any combination of clusters of one or more CPUs 1106 to be active at any given time.

[0165] In at least one embodiment, one or more CPUs 1106 may implement power management functions, including but not limited to one or more of the following features: automatic clock gating of individual hardware blocks to conserve dynamic power when idle; clock gating of each core when the core is not actively executing instructions due to executing Wait for Interrupt (“WFI”) / Wait for Event (“WFE”) instructions; power gating of each core independently; clock gating of each core cluster independently when all cores are clock-gated or power-gated; and / or power gating of each core cluster independently when all cores are power-gated. In at least one embodiment, one or more CPUs 1106 may further implement an enhanced algorithm for managing power states, wherein allowed power states and expected wake-up times are specified, and the hardware / microcode determines the optimal power state to be input for the core, cluster, and CCPLEX. In at least one embodiment, the processing core may support a simplified power state entry sequence in software, wherein work is offloaded to the microcode.

[0166] In at least one embodiment, one or more GPUs 1108 may include an integrated GPU (which may alternatively be referred to herein as an "iGPU"). In at least one embodiment, one or more GPUs 1108 may be programmable and efficient for parallel workloads. In at least one embodiment, one or more GPUs 1108 may use an enhanced tensor instruction set. In at least one embodiment, one or more GPUs 1108 may include one or more streaming microprocessors, wherein each streaming microprocessor may include a Level 1 ("L1") cache (e.g., an L1 cache with at least 96KB of storage capacity), and two or more streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512KB of storage capacity). In at least one embodiment, one or more GPUs 1108 may include at least eight streaming microprocessors. In at least one embodiment, one or more GPUs 1108 may use one or more computation application programming interfaces (APIs). In at least one embodiment, one or more GPUs 1108 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA model).

[0167] In at least one embodiment, one or more GPUs 1108 can be power-optimized for optimal performance in automotive and embedded use cases. For example, in at least one embodiment, one or more GPUs 1108 can be fabricated on FinFET (“FinFET”) circuitry. In at least one embodiment, each streaming microprocessor can include multiple mixed-precision processing cores partitioned into multiple blocks. For example, but not limited to, 64 PF32 cores and 32 PF64 cores can be partitioned into four processing blocks. In at least one embodiment, each processing block can be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA Tensor cores for deep learning matrix arithmetic, a level-zero (“L0”) instruction cache, a thread bundle scheduler, a dispatch unit, and / or a 64KB register file. In at least one embodiment, the streaming microprocessor can include independent parallel integer and floating-point data paths for employing a mix of computation and addressing operations to provide efficient execution of workloads. In at least one embodiment, the streaming microprocessor can include independent thread scheduling capabilities to enable finer-grained synchronization and collaboration between parallel threads. In at least one embodiment, the streaming microprocessor may include a combined L1 data cache and shared memory unit to improve performance while simplifying programming.

[0168] In at least one embodiment, one or more GPUs 1108 may include high-bandwidth memory (“HBM”) and / or a 16GB HBM2 memory subsystem, used in some examples to provide a peak memory bandwidth of approximately 900GB / s. In at least one embodiment, in addition to or instead of HBM memory, synchronous graphics random access memory (“SGRAM”), such as fifth-generation graphics double data rate type synchronous random access memory (“GDDR5”), may also be used.

[0169] In at least one embodiment, one or more GPUs 1108 may include unified memory technology. In at least one embodiment, address translation service (“ATS”) support can be used to allow one or more GPUs 1108 to directly access the page tables of one or more CPUs 1106. In at least one embodiment, when the memory management unit (“MMU”) of one or more GPUs 1108 experiences a miss, an address translation request can be sent to one or more CPUs 1106. In response, in at least one embodiment, two of the CPUs 1106 can look up the virtual-physical mapping of the address in their page tables and send the translation back to one or more GPUs 1108. In at least one embodiment, unified memory technology can allow a single unified virtual address space to be used for the memory of both one or more CPUs 1106 and one or more GPUs 1108, thereby simplifying the programming of one or more GPUs 1108 and the porting of applications to one or more GPUs 1108.

[0170] In at least one embodiment, one or more GPUs 1108 may include any number of access counters that can track the frequency with which one or more GPUs 1108 access the memory of other processors. In at least one embodiment, one or more access counters can help ensure that memory pages are moved to the physical memory of the processor that accesses the pages most frequently, thereby improving the efficiency of sharing memory ranges among processors.

[0171] In at least one embodiment, one or more SoCs 1104 may include any number of caches 1112, including those described herein. For example, in at least one embodiment, one or more caches 1112 may include a Level 3 (“L3”) cache that can be used for both one or more CPUs 1106 and one or more GPUs 1108 (e.g., connected to one or more CPUs 1106 and one or more GPUs 1108). In at least one embodiment, one or more caches 1112 may include a write-back cache that can track the state of each row, such as by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). In at least one embodiment, although a smaller cache size may be used, depending on the embodiment, the L3 cache may include 4 MB of memory or more.

[0172] In at least one embodiment, one or more SoCs 1104 may include one or more accelerators 1114 (e.g., hardware accelerators, software accelerators, or combinations thereof). In at least one embodiment, one or more SoCs 1104 may include a hardware acceleration cluster, which may include optimized hardware accelerators and / or large on-chip memory. In at least one embodiment, large on-chip memory (e.g., 4MB of SRAM) enables the hardware acceleration cluster to accelerate neural networks and other computations. In at least one embodiment, the hardware acceleration cluster may be used to supplement one or more GPUs 1108 and offload some tasks from one or more GPUs 1108 (e.g., to free up more cycles of one or more GPUs 1108 to perform other tasks). In at least one embodiment, one or more accelerators 1114 may be used for a target workload (e.g., perceptual, convolutional neural network (“CNN”), recurrent neural network (“RNN”), etc.) that is sufficiently stable to withstand acceleration. In at least one embodiment, the CNN may include region-based or region convolutional neural networks (“RCNN”) and fast RCNN (e.g., for object detection) or other types of CNNs.

[0173] In at least one embodiment, one or more accelerators 1114 (e.g., a hardware acceleration cluster) may include one or more deep learning accelerators (“DLAs”). In at least one embodiment, one or more DLAs may include, but are not limited to, one or more tensor processing units (“TPUs”), which may be configured to provide an additional 10 trillion operations per second for deep learning applications and inference. In at least one embodiment, the TPU may be an accelerator configured and optimized for performing image processing functions (e.g., for CNNs, RCNNs, etc.). In at least one embodiment, one or more DLAs may be further optimized for specific sets of neural network types and floating-point operations and inference. In at least one embodiment, one or more DLAs are designed to provide higher performance per millimeter than typical general-purpose GPUs and typically significantly outperform CPUs. In at least one embodiment, one or more TPUs may perform several functions, including single-instance convolution functions supporting, for example, INT8, INT16, and FP16 data types for features and weights, and post-processor functions. In at least one embodiment, one or more DLAs can execute neural networks, particularly CNNs, rapidly and efficiently on processed or unprocessed data for any of the various functions, including, but not limited to: CNNs for object recognition and detection using data from camera sensors; CNNs for distance estimation using data from camera sensors; CNNs for emergency vehicle detection, recognition, and identification using data from microphones; CNNs for face recognition and vehicle owner recognition using data from camera sensors; and / or CNNs for protection and / or safety-related events.

[0174] In at least one embodiment, one or more DLAs can perform any function of one or more GPUs 1108, and by using inference accelerators, for example, the designer can target one or more DLAs or one or more GPUs 1108 for any function. For example, in at least one embodiment, the designer can concentrate the CNN processing and floating-point operations on one or more DLAs, leaving other functions to one or more GPUs 1108 and / or one or more accelerators 1114.

[0175] In at least one embodiment, one or more accelerators 1114 may include programmable vision accelerators (“PVAs”), which may alternatively be referred to herein as computer vision accelerators. In at least one embodiment, a PVA may be designed and configured to accelerate computer vision algorithms for advanced driver assistance systems (“ADAS”) 1138, autonomous driving, augmented reality (“AR”) applications, and / or virtual reality (“VR”) applications. In at least one embodiment, a PVA may provide a balance between performance and flexibility. For example, in at least one embodiment, each PVA may include, for example, but not limited to, any number of reduced instruction set computer (“RISC”) cores, direct memory access (“DMA”), and / or any number of vector processors.

[0176] In at least one embodiment, the RISC core can interact with an image sensor (e.g., the image sensor of any camera described herein), an image signal processor, etc. In at least one embodiment, each RISC core may include any amount of memory. In at least one embodiment, depending on the embodiment, the RISC core may use any of a variety of protocols. In at least one embodiment, the RISC core may execute a real-time operating system (“RTOS”). In at least one embodiment, the RISC core may be implemented using one or more integrated circuit devices, application-specific integrated circuits (“ASICs”), and / or memory devices. For example, in at least one embodiment, the RISC core may include an instruction cache and / or tightly coupled RAM.

[0177] In at least one embodiment, DMA enables components of the PVA to access system memory independently of one or more CPUs 1106. In at least one embodiment, DMA can support any number of features for providing optimization to the PVA, including but not limited to, support for multidimensional addressing and / or circular addressing. In at least one embodiment, DMA can support up to six or more addressing dimensions, which may include, but are not limited to, block width, block height, block depth, horizontal block step, vertical block step, and / or depth step.

[0178] In at least one embodiment, the vector processor may be a programmable processor designed to efficiently and flexibly perform programming for computer vision algorithms and provide signal processing capabilities. In at least one embodiment, the PVA may include a PVA core and two vector processing subsystem partitions. In at least one embodiment, the PVA core may include a processor subsystem, one or more DMA engines (e.g., two DMA engines), and / or other peripherals. In at least one embodiment, the vector processing subsystem may operate as the main processing engine of the PVA and may include a vector processing unit (“VPU”), an instruction cache, and / or a vector memory (e.g., “VMEM”). In at least one embodiment, the VPU core may include a digital signal processor, such as, for example, a Single Instruction Multiple Data (“SIMD”) or Very Long Instruction Word (“VLIW”) digital signal processor. In at least one embodiment, the combination of SIMD and VLIW can improve throughput and speed.

[0179] In at least one embodiment, each vector processor may include an instruction cache and may be coupled to dedicated memory. Therefore, in at least one embodiment, each vector processor may be configured to execute independently of other vector processors. In at least one embodiment, the vector processors included in a particular PVA may be configured to employ data parallelism. For example, in at least one embodiment, multiple vector processors included in a single PVA may execute general computer vision algorithms, but on different regions of an image. In at least one embodiment, vector processors included in a particular PVA may execute different computer vision algorithms simultaneously on a single image, or even execute different algorithms on a sequence of images or different portions of an image. In at least one embodiment, any number of PVAs may be included in the hardware acceleration cluster, and any number of vector processors may be included in each PVA. In at least one embodiment, the PVA may include additional error correction code (“ECC”) memory for enhancing overall system security.

[0180] In at least one embodiment, one or more accelerators 1114 may include an on-chip computer vision network and static random access memory (“SRAM”) for providing high-bandwidth, low-latency SRAM to one or more accelerators 1114. In at least one embodiment, the on-chip memory may include at least 4 MB of SRAM, comprising, for example, but not limited to, eight field-configurable memory blocks accessible to both the PVA and DLA. In at least one embodiment, each pair of memory blocks may include an advanced peripheral bus (“APB”) interface, configuration circuitry, a controller, and a multiplexer. In at least one embodiment, any type of memory may be used. In at least one embodiment, the PVA and DLA may access the memory via a backbone that provides high-speed access to the memory for the PVA and DLA. In at least one embodiment, the backbone may include an on-chip computer vision network that interconnects the PVA and DLA to the memory (e.g., using an APB).

[0181] In at least one embodiment, the on-chip computer vision network may include an interface that determines that both the PVA and DLA provide ready and valid signals before transmitting any control signals / addresses / data. In at least one embodiment, the interface may provide separate phases and separate channels for transmitting control signals / addresses / data, as well as bursty communication for continuous data transmission. In at least one embodiment, although other standards and protocols may be used, the interface may conform to the International Organization for Standardization (“ISO”) 26262 or the International Electrotechnical Commission (“IEC”) 61508 standard.

[0182] In at least one embodiment, one or more SoCs 1104 may include a real-time ray tracing hardware accelerator. In at least one embodiment, the real-time ray tracing hardware accelerator may be used to rapidly and efficiently determine the location and extent of an object (e.g., within a world model) to generate real-time visualization simulations for RADAR signal interpretation, for sound propagation synthesis and / or analysis, for simulation of SONAR systems, for general wave propagation simulation, for comparison with LIDAR data for localization and / or other functions, and / or for other purposes.

[0183] In at least one embodiment, one or more accelerators 1114 may have broad applications for autonomous driving. In at least one embodiment, PVAs may be used in critical processing stages in ADAS and autonomous vehicles. In at least one embodiment, the capabilities of PVAs with low power consumption and low latency are well-matched to algorithmic domains requiring predictable processing. In other words, PVAs perform well in semi-intensive or intensive conventional computations, even on small datasets that may require predictable runtimes with low latency and low power consumption. In at least one embodiment, such as in vehicle 1100, PVAs may be designed to run classical computer vision algorithms, as they can be efficient in object detection and integer mathematical operations.

[0184] For example, according to at least one embodiment of the technology, PVA is used to perform computer stereo vision. In at least one embodiment, a semi-global matching-based algorithm may be used in some examples, but this is not intended to be limiting. In at least one embodiment, applications for Level 3-5 autonomous driving use motion estimation / stereo matching (e.g., structure reconstruction from motion, pedestrian recognition, lane detection, etc.) during operation. In at least one embodiment, PVA can perform computer stereo vision functions on input from two monocular cameras.

[0185] In at least one embodiment, the PVA can be used to perform intensive optical flow. For example, in at least one embodiment, the PVA can process raw RADAR data (e.g., using 4D Fast Fourier Transform) to provide processed RADAR data. In at least one embodiment, for example, the PVA is used to perform time-of-flight depth processing by processing raw time-of-flight data to provide processed time-of-flight data.

[0186] In at least one embodiment, the DLA can be used to run any type of network to enhance control and driving safety, including, but not limited to, neural networks, whose output is a measurement of confidence for each object detection. In at least one embodiment, the confidence can be represented or interpreted as a probability, or as providing a relative “weight” for each detection relative to other detections. In at least one embodiment, the confidence measurement enables the system to make further decisions about which detections should be considered true positives rather than false positives. In at least one embodiment, the system can set a threshold for the confidence and only consider detections exceeding the threshold as true positives. In embodiments using an Automatic Emergency Braking (“AEB”) system, false positives would cause the vehicle to automatically perform emergency braking, which is obviously undesirable. In at least one embodiment, a highly confident detection can be considered a trigger for AEB. In at least one embodiment, the DLA can run a neural network for regressing the confidence values. In at least one embodiment, the neural network may take at least a subset of parameters as its input, such as bounding box size, ground plane estimate (e.g., from another subsystem), and outputs of one or more IMU sensors 1166 related to vehicle 1100 orientation, distance, and object 3D position estimates obtained from the neural network and / or other sensors (e.g., one or more LiDAR sensors 1164 or one or more RADAR sensors 1160).

[0187] In at least one embodiment, one or more SoCs 1104 may include one or more data stores 1116 (e.g., memory). In at least one embodiment, one or more data stores 1116 may be on-chip memory of one or more SoCs 1104, which may store neural networks to be executed on one or more GPUs 1108 and / or DLAs. In at least one embodiment, one or more data stores 1116 may have a sufficiently large capacity to store multiple instances of the neural network for redundancy and security. In at least one embodiment, one or more data stores 1116 may include one or more L2 or L3 caches.

[0188] In at least one embodiment, one or more SoCs 1104 may include any number of processors 1110 (e.g., embedded processors). In at least one embodiment, one or more processors 1110 may include a startup and power management processor, which may be a dedicated processor and subsystem for handling startup power and management functions, as well as associated security execution. In at least one embodiment, the startup and power management processor may be part of a startup sequence of one or more SoCs 1104 and may provide runtime power management services. In at least one embodiment, the startup power and management processor may provide clock and voltage programming, assist system low-power state transitions, thermal and temperature sensor management of one or more SoCs 1104s, and / or power state management of one or more SoCs 1104s. In at least one embodiment, each temperature sensor may be implemented with its output frequency proportional to temperature, and one or more SoCs 1104s may use the ring oscillator to detect the temperature of one or more CPUs 1106, one or more GPUs 1108, and / or one or more accelerators 1114. In at least one embodiment, if it is determined that the temperature exceeds a threshold, the startup and power management processor may enter a temperature fault routine and place one or more SoCs 1104 in a lower power state and / or place the vehicle 1100 in a driver safe parking mode (e.g., bring the vehicle 1100 to a safe stop).

[0189] In at least one embodiment, one or more processors 1110 may further include a set of embedded processors that can be used as an audio processing engine. The audio processing engine may be an audio subsystem that provides full hardware support for multi-channel audio through multiple interfaces and a wide and flexible range of audio I / O interfaces. In at least one embodiment, the audio processing engine is a dedicated processor core with a digital signal processor having dedicated RAM.

[0190] In at least one embodiment, one or more processors 1110 may further include an always-on processor engine that can provide the necessary hardware features to support low-power sensor management and wake-up use cases. In at least one embodiment, the always-on processor engine may include, but is not limited to, a processor core, tightly coupled RAM, support for peripherals (e.g., timers and interrupt controllers), various I / O controller peripherals, and routing logic.

[0191] In at least one embodiment, one or more processors 1110 may further include a secure cluster engine, which includes, but is not limited to, a dedicated processor subsystem for handling security management of automotive applications. In at least one embodiment, the secure cluster engine may include, but is not limited to, two or more processor cores, tightly coupled RAM, supporting peripherals (e.g., timers, interrupt controllers, etc.) and / or routing logic. In secure mode, in at least one embodiment, the two or more cores may operate in lockstep mode and may be used as a single core with comparison logic for detecting any differences between their operations. In at least one embodiment, one or more processors 1110 may further include a real-time camera engine, which may include, but is not limited to, a dedicated processor subsystem for handling real-time camera management. In at least one embodiment, one or more processors 1110 may further include a high dynamic range signal processor, which may include, but is not limited to, an image signal processor, which is a hardware engine as part of the camera processing pipeline.

[0192] In at least one embodiment, one or more processors 1110 may include a video image synthesizer, which may be a processing block (e.g., implemented on a microprocessor) that implements video playback applications to generate final images for video post-processing functions required by the player window. In at least one embodiment, the video image synthesizer may perform lens distortion correction on one or more wide-angle cameras 1170, one or more surround cameras 1174, and / or one or more in-cabin monitoring camera sensors. In at least one embodiment, preferably, the in-cabin monitoring camera sensors are monitored by a neural network running on another instance of SoC 1104, the neural network being configured to recognize in-cabin events and respond accordingly. In at least one embodiment, the in-cabin system may perform, but is not limited to, lip reading to activate cellular service and make phone calls, instruct emails, change the vehicle's destination, activate or change the vehicle's infotainment system and settings, or provide voice-activated web browsing. In at least one embodiment, certain functions are available to the driver when the vehicle is operating in autonomous mode, and are otherwise disabled.

[0193] In at least one embodiment, the video image synthesizer may include enhanced temporal denoising for both spatial and temporal noise reduction. For example, in at least one embodiment, in the case of motion in the video, denoising appropriately weights spatial information to reduce the weight of information provided by adjacent frames. In at least one embodiment, in the case where the image or a portion of the image does not contain motion, temporal denoising performed by the video image synthesizer may use information from previous images to reduce noise in the current image.

[0194] In at least one embodiment, the video image compositor can also be configured to perform stereoscopic correction on the input stereoscopic shot frames. In at least one embodiment, when using an operating system desktop, the video image compositor can also be used for user interface compositing, and does not require one or more GPUs 1108 to continuously render new surfaces. In at least one embodiment, when one or more GPUs 1108 are powered and active for 3D rendering, the video image compositor can be used to offload one or more GPUs 1108 to improve performance and responsiveness.

[0195] In at least one embodiment, one or more SoCs of SoC 1104 may further include a Mobile Industrial Processor Interface (“MIPI”) camera serial interface, a high-speed interface, and / or a video input block that can be used for receiving video and input from a camera and associated pixel input functions. In at least one embodiment, one or more SoCs of SoC 1104 may further include an input / output controller that can be software controlled and can be used to receive I / O signals not assigned to a specific role.

[0196] In at least one embodiment, one or more SoCs of SoC 1104 may further include extensive peripheral interfaces for enabling communication with peripheral devices, audio encoders / decoders (“codecs”), power management and / or other devices. In at least one embodiment, one or more SoCs of SoC 1104 may be used to process data from (e.g., connected via gigabit multimedia serial links and Ethernet channels) cameras, sensors (e.g., one or more LiDAR sensors 1164, one or more RADAR sensors 1160, etc., which may be connected via Ethernet channels), data from bus 1102 (e.g., vehicle 1100 speed, steering wheel position, etc.), data from one or more GNSS sensors 1158 (e.g., connected via Ethernet bus or CAN bus), etc. In at least one embodiment, one or more SoCs of SoC 1104 may further include a dedicated high-performance large-scale memory controller, which may include its own DMA engine and may be used to free one or more CPUs of SoC 1106 from routine data management tasks.

[0197] In at least one embodiment, one or more SoCs 1104 can be an end-to-end platform with a flexible architecture spanning automation levels 3-5, providing a comprehensive functional safety architecture that leverages and effectively utilizes computer vision and ADAS technologies to achieve diversity and redundancy, and provides a platform for a flexible, reliable driving software stack and deep learning tools. In at least one embodiment, one or more SoCs 1104 can be faster, more reliable, and even more energy- and space-efficient than conventional systems. For example, in at least one embodiment, one or more accelerators 1114, when combined with one or more CPUs 1106, one or more GPUs 1108, and one or more data storage 1116, can provide a fast and efficient platform for Level 3-5 autonomous vehicles.

[0198] In at least one embodiment, the computer vision algorithm can be executed on a CPU, which can be configured using a high-level programming language (e.g., C) to execute various processing algorithms on a variety of visual data. However, in at least one embodiment, the CPU typically cannot meet the performance requirements of many computer vision applications, such as performance requirements related to execution time and power consumption. In at least one embodiment, many CPUs cannot execute complex object detection algorithms in real time, which are used in automotive ADAS applications and practical Level 3-5 autonomous vehicles.

[0199] The embodiments described herein allow for the simultaneous and / or sequential execution of multiple neural networks and allow for the combination of results to achieve Level 3-5 autonomous driving capabilities. For example, in at least one embodiment, a CNN executed on a DLA or discrete GPU (e.g., one or more GPUs 1120) may include text and word recognition, thereby allowing the reading and understanding of traffic signs, including signs for which the neural network has not yet been specifically trained. In at least one embodiment, the DLA may also include a neural network capable of recognizing, interpreting, and providing semantic understanding of signs, and passing this semantic understanding to a path planning module running on a CPU complex.

[0200] In at least one embodiment, multiple neural networks can run simultaneously for Level 3, 4, or 5 driving. For example, in at least one embodiment, a warning sign stating "Caution: flashing lights indicate icy conditions," along with the lights, can be interpreted independently or jointly by several neural networks. In at least one embodiment, the warning sign itself can be recognized as a traffic sign by a first deployed neural network (e.g., a trained neural network), and the text "flashing lights indicate icy conditions" can be interpreted by a second deployed neural network, which informs the vehicle's path planning software (preferably executing on a CPU complex) that icy conditions exist when flashing lights are detected. In at least one embodiment, flashing lights can be identified by operating a third deployed neural network across multiple frames, informing the vehicle's path planning software of the presence (or absence) of flashing lights. In at least one embodiment, all three neural networks can run simultaneously, for example within the DLA and / or on one or more GPUs 1108.

[0201] In at least one embodiment, the CNN for face recognition and vehicle owner identification can use data from camera sensors to identify the presence of an authorized driver and / or the owner of vehicle 1100. In at least one embodiment, a normally open sensor processing engine can be used to unlock the vehicle when the owner approaches the driver's door and turns on the lights, and in a safe mode, it can be used to disable the vehicle when the owner leaves it. In this way, one or more SoCs 1104 provide protection against theft and / or carjacking.

[0202] In at least one embodiment, the CNN for emergency vehicle detection and identification can use data from microphone 1196 to detect and identify emergency vehicle sirens. In at least one embodiment, one or more SoCs 1104 use the CNN to classify environmental and urban sounds, as well as visual data. In at least one embodiment, the CNN running on DLA is trained to identify the relative approach speed of emergency vehicles (e.g., by using the Doppler effect). In at least one embodiment, the CNN can also be trained to identify emergency vehicles specific to the local area in which the vehicle is operating, such as those identified by one or more GNSS sensors 1158. In at least one embodiment, when operating in Europe, the CNN will seek to detect European sirens, while in North America, the CNN will seek to identify only North American sirens. In at least one embodiment, once an emergency vehicle is detected, a control program can be used, with the assistance of one or more ultrasonic sensors 1162, to execute emergency vehicle safety routines, such as slowing the vehicle, pulling the vehicle to the side of the road, stopping, and / or idling the vehicle until the emergency vehicle passes.

[0203] In at least one embodiment, vehicle 1100 may include one or more CPUs 1118 (e.g., one or more discrete CPUs or one or more dCPUs) that may be coupled to one or more SoCs 1104 via high-speed interconnects (e.g., PCIe). In at least one embodiment, one or more CPUs 1118 may include, for example, an x86 processor. One or more CPUs 1118 may be used to perform any of a variety of functions, such as arbitrating potentially inconsistent results between ADAS sensors and one or more SoCs 1104, and / or monitoring the status and health of one or more controllers 1136 and / or the on-chip infotainment system (“infotainment SoC”) 1130.

[0204] In at least one embodiment, vehicle 1100 may include one or more GPUs 1120 (e.g., one or more discrete GPUs or one or more dGPUs) coupled to one or more SoCs 1104 via high-speed interconnects (e.g., NVIDIA's NVLINK channels). In at least one embodiment, one or more GPUs 1120 may provide additional artificial intelligence capabilities, such as by executing redundant and / or different neural networks, and may be used to train and / or update neural networks based at least in part on inputs from sensors of vehicle 1100 (e.g., sensor data).

[0205] In at least one embodiment, vehicle 1100 may further include a network interface 1124, which may include, but is not limited to, one or more wireless antennas 1126 (e.g., one or more wireless antennas for different communication protocols, such as cellular antennas, Bluetooth antennas, etc.). In at least one embodiment, network interface 1124 may be used to enable wireless connectivity to Internet cloud services (e.g., with servers and / or other network devices), with other vehicles, and / or with computing devices (e.g., passenger client devices). In at least one embodiment, for communication with other vehicles, a direct link and / or an indirect link (e.g., via a network and the Internet) may be established between vehicle 1100 and another vehicle. In at least one embodiment, a vehicle-to-vehicle communication link may be used to provide a direct link. In at least one embodiment, the vehicle-to-vehicle communication link may provide vehicle 1100 with information about vehicles near vehicle 1100 (e.g., vehicles in front, to the side, and / or behind vehicle 1100). In at least one embodiment, the foregoing functionality may be part of a cooperative adaptive cruise control function of vehicle 1100.

[0206] In at least one embodiment, network interface 1124 may include a System-on-Chip (SoC) that provides modulation and demodulation functions and enables one or more controllers 1136 to communicate over a wireless network. In at least one embodiment, network interface 1124 may include a radio frequency (RF) front-end for up-conversion from baseband to RF and down-conversion from RF to baseband. In at least one embodiment, frequency conversion may be performed in any technically feasible manner. For example, frequency conversion may be performed using known processes and / or using a superheterodyne process. In at least one embodiment, the RF front-end functionality may be provided by a separate chip. In at least one embodiment, the network interface may include wireless functions for communication over LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0207] In at least one embodiment, vehicle 1100 may further include one or more data storage units 1128, which may include, but are not limited to, off-chip (e.g., one or more off-chip SoC 1104) storage. In at least one embodiment, one or more data storage units 1128 may include, but are not limited to, one or more storage elements, including RAM, SRAM, dynamic random access memory (“DRAM”), video random access memory (“VRAM”), flash memory, hard disk, and / or other components and / or devices capable of storing at least one bit of data.

[0208] In at least one embodiment, vehicle 1100 may further include one or more GNSS sensors 1158 (e.g., GPS and / or auxiliary GPS sensors) to assist in mapping, sensing, occupancy grid generation, and / or path planning functions. In at least one embodiment, any number of GNSS sensors 1158 may be used, including, for example, but not limited to, GPS sensors using a USB connector with an Ethernet-to-serial interface (e.g., RS-232) bridge.

[0209] In at least one embodiment, vehicle 1100 may further include one or more RADAR sensors 1160. In at least one embodiment, one or more RADAR sensors 1160 may be used by vehicle 1100 for remote vehicle detection, even in dark and / or inclement weather conditions. In at least one embodiment, the RADAR functional safety level may be ASIL B. In at least one embodiment, one or more RADAR sensors 1160 may use a CAN bus and / or bus 1102 (e.g., for transmitting data generated by one or more RADAR sensors 1160) for control and access to object tracking data, and in some examples may access an Ethernet channel to access raw data. In at least one embodiment, a wide variety of RADAR sensor types may be used. For example, but not limited to, one or more RADAR sensors 1160 may be suitable for front, rear, and side RADAR use. In at least one embodiment, one or more of the RADAR sensors 1160 are pulse Doppler RADAR sensors.

[0210] In at least one embodiment, one or more RADAR sensors 1160 may include different configurations, such as long-range with a narrow field of view, short-range with a wide field of view, short-range side coverage, etc. In at least one embodiment, the long-range RADAR can be used for adaptive cruise control functions. In at least one embodiment, the long-range RADAR system can provide a wide field of view achieved through two or more independent scans (e.g., within a 250m range). In at least one embodiment, one or more RADAR sensors 1160 can help distinguish between static and moving objects and can be used by the ADAS system 1138 for emergency braking assistance and forward collision warning. In at least one embodiment, one or more sensors 1160 included in the long-range RADAR system may include, but are not limited to, a monostatic multimodal RADAR with multiple (e.g., six or more) fixed RADAR antennas and high-speed CAN and FlexRay interfaces. In at least one embodiment, employing six antennas, the four central antennas can create a focused beammap designed to record the environment surrounding the vehicle 1100 at a high speed while minimizing traffic interference from adjacent lanes. In at least one embodiment, the other two antennas can expand the field of view, thereby enabling them to quickly detect vehicles entering or leaving the lane of vehicle 1100.

[0211] In at least one embodiment, as an example, a mid-range RADAR system may include a range of up to 160m (front) or 80m (rear) and a field of view of up to 42 degrees (front) or 150 degrees (rear). In at least one embodiment, a short-range RADAR system may include, but is not limited to, any number of RADAR sensors 1160 designed to be mounted at both ends of the rear bumper. When mounted at both ends of the rear bumper, in at least one embodiment, the RADAR sensor system may generate two beams that continuously monitor the rearward direction of the vehicle and nearby blind spots. In at least one embodiment, the short-range RADAR system may be used in ADAS system 1138 for blind spot detection and / or lane change assistance.

[0212] In at least one embodiment, vehicle 1100 may further include one or more ultrasonic sensors 1162. In at least one embodiment, one or more ultrasonic sensors 1162, which may be positioned at the front, rear, and / or sides of vehicle 1100, may be used for parking assistance and / or creating and updating occupancy grids. In at least one embodiment, a wide variety of ultrasonic sensors 1162 may be used, and different ultrasonic sensors 1162 may be used for different detection ranges (e.g., 2.5m, 4m). In at least one embodiment, the ultrasonic sensors 1162 may operate at ASIL B functional safety level.

[0213] In at least one embodiment, vehicle 1100 may include one or more LiDAR sensors 1164. In at least one embodiment, one or more LiDAR sensors 1164 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. In at least one embodiment, one or more LiDAR sensors 1164 may operate at functional safety level ASIL B. In at least one embodiment, vehicle 1100 may include multiple (e.g., two, four, six, etc.) LiDAR sensors 1164 that can use Ethernet channels (e.g., to provide data to a Gigabit Ethernet switch).

[0214] In at least one embodiment, one or more LiDAR sensors 1164 may be able to provide a list of objects and their distances for a 360-degree field of view. In at least one embodiment, one or more commercially available LiDAR sensors 1164 may, for example, have an advertising range of approximately 100m, an accuracy of 2cm-3cm, and support a 100Mbps Ethernet connection. In at least one embodiment, one or more non-protruding LiDAR sensors may be used. In such an embodiment, one or more LiDAR sensors 1164 may include small devices that can be embedded in the front, rear, side, and / or corner locations of vehicle 1100. In at least one embodiment, one or more LiDAR sensors 1164, in such an embodiment, may provide up to 120 degrees of horizontal field of view and 35 degrees of vertical field of view, even for objects with low reflectivity, and have a range of 200m. In at least one embodiment, one or more forward-mounted LiDAR sensors 1164 may be configured for a horizontal field of view between 45 degrees and 135 degrees.

[0215] In at least one embodiment, LIDAR technology (such as 3D flash LIDAR) may also be used. In at least one embodiment, 3D flash LIDAR uses a laser flash as a transmission source to illuminate the area around vehicle 1100 up to approximately 200m. In at least one embodiment, the flash LIDAR unit includes, but is not limited to, a receiver that records the propagation time of the laser pulse and the reflected light on each pixel, which in turn corresponds to the range from vehicle 1100 to the object. In at least one embodiment, flash LIDAR can allow the generation of highly accurate and distortion-free images of the surrounding environment using each laser flash. In at least one embodiment, four flash LIDAR sensors may be deployed, one on each side of vehicle 1100. In at least one embodiment, the 3D flash LIDAR system includes, but is not limited to, a solid-state 3D staring array LIDAR camera with no moving parts other than a fan (e.g., a non-scanning LIDAR device). In at least one embodiment, the flash LIDAR device can use a 5-nanosecond Class I (eye-safe) laser pulse per frame and can capture reflected laser light as a 3D ranging point cloud and co-registered intensity data.

[0216] In at least one embodiment, vehicle 1100 may further include one or more IMU sensors 1166. In at least one embodiment, one or more IMU sensors 1166 may be located at the center of the rear axle of vehicle 1100. In at least one embodiment, one or more IMU sensors 1166 may include, for example, but not limited to, one or more accelerometers, one or more magnetometers, one or more gyroscopes, one or more magnetic compasses, and / or other sensor types. In at least one embodiment, for example in a six-axis application, one or more IMU sensors 1166 may include, but are not limited to, accelerometers and gyroscopes. In at least one embodiment, for example in a nine-axis application, one or more IMU sensors 1166 may include, but are not limited to, accelerometers, gyroscopes, and magnetometers.

[0217] In at least one embodiment, one or more IMU sensors 1166 can be implemented as a miniature, high-performance GPS-assisted inertial navigation system (“GPS / INS”) combining a microelectromechanical system (“MEMS”) inertial sensor, a high-sensitivity GPS receiver, and an advanced Kalman filtering algorithm to provide estimates of position, velocity, and attitude. In at least one embodiment, one or more IMU sensors 1166 enable vehicle 1100 to estimate its heading by directly observing and correlating velocity changes from GPS to one or more IMU sensors 1166, without requiring input from a magnetic sensor. In at least one embodiment, one or more IMU sensors 1166 and one or more GNSS sensors 1158 can be combined in a single integrated unit.

[0218] In at least one embodiment, vehicle 1100 may include one or more microphones 1196 placed inside and / or around vehicle 1100. In at least one embodiment, one or more microphones 1196 may be used for emergency vehicle detection and identification.

[0219] In at least one embodiment, vehicle 1100 may further include any number of camera types, including one or more stereo cameras 1168, one or more wide-angle cameras 1170, one or more infrared cameras 1172, one or more surround cameras 1174, one or more long-range cameras 1198, one or more mid-range cameras 1176, and / or other camera types. In at least one embodiment, the cameras can be used to capture image data around the entire perimeter of vehicle 1100. In at least one embodiment, the type of camera used depends on vehicle 1100. In at least one embodiment, any combination of camera types can be used to provide the necessary coverage around vehicle 1100. In at least one embodiment, the number of cameras deployed may vary depending on the embodiment. For example, in at least one embodiment, vehicle 1100 may include six cameras, seven cameras, ten cameras, twelve cameras, or other numbers of cameras. In at least one embodiment, the cameras may support, by way of example but not limited to, gigabit multimedia serial link (“GMSL”) and / or gigabit Ethernet communication. In at least one embodiment, references previously made herein... Figure 11A and Figure 11B Each camera is described in more detail.

[0220] In at least one embodiment, vehicle 1100 may further include one or more vibration sensors 1142. In at least one embodiment, one or more vibration sensors 1142 may measure vibrations of components of vehicle 1100 (e.g., axles). For example, in at least one embodiment, changes in vibration may indicate changes in road surface. In at least one embodiment, when two or more vibration sensors 1142 are used, differences between vibrations may be used to determine road surface friction or slippage (e.g., when there is a vibration difference between a power drive axle and a free-rotating axle).

[0221] In at least one embodiment, vehicle 1100 may include ADAS system 1138. In at least one embodiment, ADAS system 1138 may include, but is not limited to, SoC in some examples. In at least one embodiment, ADAS system 1138 may include, but is not limited to, any number and any combination of autonomous / adaptive / automatic cruise control (“ACC”) system, cooperative adaptive cruise control (“CACC”) system, forward collision warning (“FCW”) system, automatic emergency braking (“AEB”) system, lane departure warning (“LDW”) system, lane keeping assist (“LKA”) system, blind spot warning (“BSW”) system, rear cross traffic warning (“RCTW”) system, collision warning (“CW”) system, lane centering (“LC”) system and / or other systems, features and / or functions.

[0222] In at least one embodiment, the ACC system may use one or more RADAR sensors 1160, one or more LIDAR sensors 1164, and / or any number of cameras. In at least one embodiment, the ACC system may include a longitudinal ACC system and / or a lateral ACC system. In at least one embodiment, the longitudinal ACC system monitors and controls the distance to another vehicle immediately in front of vehicle 1100 and automatically adjusts the speed of vehicle 1100 to maintain a safe distance from the vehicle ahead. In at least one embodiment, the lateral ACC system performs distance holding and suggests that vehicle 1100 change lanes if necessary. In at least one embodiment, lateral ACC is associated with other ADAS applications, such as LC and CW.

[0223] In at least one embodiment, the CACC system uses information from other vehicles, which may be received indirectly from other vehicles via a wireless link or through a network connection (e.g., via the Internet) via network interface 1124 and / or one or more wireless antennas 1126. In at least one embodiment, the direct link may be provided by a vehicle-to-vehicle (“V2V”) communication link, while the indirect link may be provided by an infrastructure-to-vehicle (“I2V”) communication link. Typically, V2V communication provides information about the vehicle immediately ahead (e.g., a vehicle immediately in front of vehicle 1100 and in the same lane as it), while I2V communication provides information about traffic further ahead. In at least one embodiment, the CACC system may include one or both of the I2V and V2V information sources. In at least one embodiment, given information about the vehicles ahead of vehicle 1100, the CACC system can be more reliable and has the potential to improve traffic flow smoothness and reduce road congestion.

[0224] In at least one embodiment, the FCW system is designed to warn the driver of danger so that the driver can take corrective action. In at least one embodiment, the FCW system uses a forward-facing camera and / or one or more RADAR sensors 1160 coupled to a dedicated processor, DSP, FPGA, and / or ASIC, which are electrically coupled to provide driver feedback, such as a display, speaker, and / or vibration components. In at least one embodiment, the FCW system can provide warnings, such as in the form of audible, visual, haptic, and / or rapid braking pulses.

[0225] In at least one embodiment, the AEB system detects an impending forward collision with another vehicle or other object and can automatically apply braking if the driver does not take corrective action within a specified time or distance parameter. In at least one embodiment, the AEB system may use one or more forward-facing cameras and / or one or more RADAR sensors 1160 coupled to a dedicated processor, DSP, FPGA, and / or ASIC. In at least one embodiment, when the AEB system detects a hazard, it typically first warns the driver to take corrective action to avoid a collision, and if the driver does not take corrective action, the AEB system may automatically apply braking to attempt to prevent or at least mitigate the effects of the predicted collision. In at least one embodiment, the AEB system may include techniques such as dynamic brake support and / or collision proximity braking.

[0226] In at least one embodiment, when vehicle 1100 crosses lane markings, the LDW system provides visual, auditory, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver. In at least one embodiment, the LDW system is not activated when the driver indicates intentional lane departure, such as by activating a turn signal. In at least one embodiment, the LDW system may use a front-facing camera coupled to a dedicated processor, DSP, FPGA, and / or ASIC, which is electrically coupled to provide driver feedback such as a display, speaker, and / or vibration components. In at least one embodiment, the LKA system is a variant of the LDW system. In at least one embodiment, if vehicle 1100 begins to leave its lane, the LKA system provides steering input or braking to correct vehicle 1100.

[0227] In at least one embodiment, the BSW system detects and warns the driver that the vehicle is in the blind spot of the car. In at least one embodiment, the BSW system can provide visual, auditory, and / or tactile alerts to indicate that merging or changing lanes is unsafe. In at least one embodiment, the BSW system can provide additional warnings when the driver uses turn signals. In at least one embodiment, the BSW system can use one or more rear-facing cameras and / or one or more RADAR sensors 1160 coupled to a dedicated processor, DSP, FPGA, and / or ASIC, electrically coupled to driver feedback such as a display, speaker, and / or vibration components.

[0228] In at least one embodiment, the RCTW system can provide visual, auditory, and / or tactile notifications when the vehicle 1100 detects an object outside the range of the rear camera while reversing. In at least one embodiment, the RCTW system includes an AEB system to ensure that vehicle braking is applied to avoid a collision. In at least one embodiment, the RCTW system may use one or more rear-facing RADAR sensors 1160 coupled to a dedicated processor, DSP, FPGA, and / or ASIC, which are electrically coupled to provide driver feedback such as displays, speakers, and / or vibration components.

[0229] In at least one embodiment, conventional ADAS systems may be prone to generating false alarms, which can be annoying and distracting to the driver, but are generally not catastrophic because conventional ADAS systems alert the driver and allow the driver to determine whether a safe situation truly exists and take appropriate action. In at least one embodiment, in the event of conflicting results, vehicle 1100 decides for itself whether to follow the result of the main computer or auxiliary computer (e.g., the first or second controller in controller 1136). For example, in at least one embodiment, ADAS system 1138 may be a backup and / or auxiliary computer for providing perception information to a backup computer rationality module. In at least one embodiment, a backup computer rationality monitor may run redundant software on hardware components to detect faults in perception and dynamic driving tasks. In at least one embodiment, output from ADAS system 1138 may be provided to a supervisory MCU. In at least one embodiment, if output from the main computer and output from the auxiliary computer conflict, the supervisory MCU determines how to reconcile the conflict to ensure safe operation.

[0230] In at least one embodiment, the master computer may be configured to provide a confidence score to the supervisory MCU, indicating the master computer's confidence in the selected result. In at least one embodiment, if the confidence score exceeds a threshold, the supervisory MCU may follow the master computer's instructions regardless of whether the auxiliary computer provides conflicting or inconsistent results. In at least one embodiment, if the confidence score does not meet the threshold, and if the master computer and the auxiliary computer indicate different results (e.g., conflict), the supervisory MCU may arbitrate between the computers to determine the appropriate result.

[0231] In at least one embodiment, the supervisory MCU may be configured to run a neural network trained and configured to determine, at least in part, the conditions under which the auxiliary computer provides a false alarm based on outputs from a host computer and an auxiliary computer. In at least one embodiment, one or more neural networks in the supervisory MCU may learn when the outputs of the auxiliary computer can be trusted and when they cannot. For example, in at least one embodiment, when the auxiliary computer is a RADAR-based FCW system, one or more neural networks in the supervisory MCU may learn when the FCW system is recognizing a metallic object that is not actually dangerous, such as a drain grille or manhole cover that would trigger an alarm. In at least one embodiment, when the auxiliary computer is a camera-based LDW system, the neural network in the supervisory MCU may learn to override LDW when a cyclist or pedestrian is present and lane departure is actually the safest operation. In at least one embodiment, the supervisory MCU may include at least one of a DLA or GPU suitable for running one or more neural networks with associated memory. In at least one embodiment, the supervisory MCU may include and / or be included as a component of one or more SoC 1104s.

[0232] In at least one embodiment, the ADAS system 1138 may include an auxiliary computer that performs ADAS functions using conventional computer vision rules. In at least one embodiment, the auxiliary computer may use classic computer vision rules (if-then), and the presence of one or more neural networks in the supervisory MCU can improve reliability, security, and performance. For example, in at least one embodiment, diverse implementations and intentional non-identity make the entire system more fault-tolerant, especially for failures caused by software (or software-hardware interface) functionality. For example, in at least one embodiment, if a software vulnerability or bug exists in the software running on the host computer, and the not-quite-same software code running on the auxiliary computer provides consistent overall results, the supervisory MCU can have greater confidence that the overall results are correct, and that the vulnerability in the software or hardware on the host computer will not lead to a major error.

[0233] In at least one embodiment, the output of the ADAS system 1138 may be fed into the perception block and / or the dynamic driving task block of the main computer. For example, in at least one embodiment, if the ADAS system 1138 indicates a forward collision warning due to an object directly ahead, the perception block may use the information when the object is identified. In at least one embodiment, as described herein, the assistance computer may have its own neural network, which is trained to reduce the risk of false alarms.

[0234] In at least one embodiment, vehicle 1100 may further include an infotainment SoC 1130 (e.g., an in-vehicle infotainment system (IVI)). Although shown and described as an SoC, in at least one embodiment, the infotainment system SoC 1130 may not be an SoC and may include, but is not limited to, two or more discrete components. In at least one embodiment, the infotainment SoC 1130 may include, but is not limited to, a combination of hardware and software that can be used to provide audio (e.g., music, personal digital assistant, navigation instructions, news, radio, etc.), video (e.g., television, movies, streaming media, etc.), telephone (e.g., hands-free calling), network connectivity (e.g., LTE, WiFi, etc.) and / or information services (e.g., navigation system, rear parking assist, radio data system, vehicle-related information such as fuel level, total coverage distance, brake fuel level, fuel level, door opening / closing, air filter information, etc.) to vehicle 1100. For example, the infotainment SoC 1130 may include a radio, disk player, navigation system, video player, USB and Bluetooth connectivity, in-vehicle computer, in-vehicle entertainment system, WiFi, steering wheel audio controls, hands-free voice control, head-up display (“HUD”), HMI display 1134, telematics device, control panel (e.g., for controlling and / or interacting with various components, features and / or systems) and / or other components. In at least one embodiment, the infotainment SoC 1130 may further be used to provide information (e.g., visual and / or auditory information) to one or more users of the vehicle 1100, such as information from ADAS system 1138, autonomous driving information (such as planned vehicle maneuvers), trajectory, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.) and / or other information.

[0235] In at least one embodiment, the infotainment SoC 1130 may include any number and type of GPU functionality. In at least one embodiment, the infotainment SoC 1130 may communicate with other devices, systems, and / or components of the vehicle 1100 via bus 1102. In at least one embodiment, the infotainment SoC 1130 may be coupled to a supervisory MCU, enabling the GPU of the infotainment system to perform some autonomous driving functions in the event of a failure of one or more main controllers 1136 (e.g., the main computer and / or backup computer of the vehicle 1100). In at least one embodiment, the infotainment SoC 1130 may place the vehicle 1100 into a driver-to-safe parking mode, as described herein.

[0236] In at least one embodiment, vehicle 1100 may further include instrument panel 1132 (e.g., digital instrument panel, electronic instrument panel, digital instrument cluster, etc.). In at least one embodiment, instrument panel 1132 may include, but is not limited to, controllers and / or supercomputers (e.g., discrete controllers or supercomputers). In at least one embodiment, instrument panel 1132 may include, but is not limited to, any number and combination of instruments, such as speedometer, fuel level, oil pressure, tachometer, odometer, turn indicator, shift position indicator, one or more seatbelt warning lights, one or more parking brake warning lights, one or more engine malfunction lights, auxiliary restraint system (e.g., airbag) information, lighting controls, safety system controls, navigation information, etc. In some examples, information may be displayed and / or shared between infotainment SoC 1130 and instrument panel 1132. In at least one embodiment, instrument panel 1132 may be included as part of infotainment SoC 1130, or vice versa.

[0237] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 can be... Figure 11C The system is used to perform inference or prediction operations based at least in part on weight parameters calculated using the neural network training operations, neural network functions and / or architectures, or neural network use cases described herein.

[0238] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0239] Figure 11D It is based on at least one embodiment in one or more cloud-based servers and Figure 11AA diagram of a system for communication between autonomous vehicles 1100. In at least one embodiment, the system may include, but is not limited to, one or more servers 1178, one or more networks 1190, and any number and type of vehicles, including vehicle 1100. In at least one embodiment, one or more servers 1178 may include, but is not limited to, multiple GPUs 1184(A)-1184(H) (collectively referred to herein as GPU 1184), PCIe switches 1182(A)-1182(D) (collectively referred to herein as PCIe switch 1182), and / or CPUs 1180(A)-1180(B) (collectively referred to herein as CPU 1180). In at least one embodiment, GPU 1184, CPU 1180, and PCIe switch 1182 may be interconnected with high-speed interconnects, such as, for example, but not limited to, NVLink interface 1188 developed by NVIDIA and / or PCIe connection 1186. In at least one embodiment, the GPU 1184 is connected via NVLink and / or NVSwitch SoC, and the GPU 1184 and PCIe switch 1182 are connected via PCIe interconnect. Although eight GPUs 1184, two CPUs 1180, and four PCIe switches 1182 are shown, this is not intended to be limiting. In at least one embodiment, each of one or more servers 1178 may include, but is not limited to, any number of GPUs 1184, CPUs 1180, and / or PCIe switches 1182 in any combination. For example, in at least one embodiment, one or more servers 1178 may each include eight, sixteen, thirty-two, and / or more GPUs 1184.

[0240] In at least one embodiment, one or more servers 1178 may receive image data representing images from vehicles via one or more networks 1190, the images showing unexpected or changed road conditions, such as recently commenced roadworks. In at least one embodiment, one or more servers 1178 may send updated neural network 1192 and / or map information 1194 to vehicles via one or more networks 1190, including but not limited to information about traffic and road conditions. In at least one embodiment, updates to map information 1194 may include, but are not limited to, updates to HD map 1122, such as information about construction sites, potholes, sidewalks, floods, and / or other obstacles. In at least one embodiment, neural network 1192 and / or map information 1194 may be generated from new training and / or experience represented by data received from any number of vehicles in the environment, and / or at least based on training performed at a data center (e.g., using one or more servers 1178 and / or other servers).

[0241] In at least one embodiment, one or more servers 1178 may be used to train a machine learning model (e.g., a neural network) at least in part based on training data. In at least one embodiment, the training data may be generated by the vehicle, and / or may be generated in a simulation (e.g., using a game engine). In at least one embodiment, any amount of training data is labeled (e.g., where the associated neural network benefits from supervised learning) and / or undergoes other preprocessing. In at least one embodiment, no amount of training data is labeled and / or preprocessed (e.g., where the associated neural network does not require supervised learning). In at least one embodiment, once the machine learning model is trained, the machine learning model may be used by the vehicle (e.g., sent to the vehicle via one or more networks 1190, and / or the machine learning model may be used by one or more servers 1178 to remotely monitor the vehicle).

[0242] In at least one embodiment, one or more servers 1178 may receive data from the vehicle and apply the data to a state-of-the-art real-time neural network for real-time intelligent inference. In at least one embodiment, one or more servers 1178 may include a deep learning supercomputer and / or a dedicated AI computer powered by one or more GPUs 1184, such as the DGX and DGX Station machines developed by NVIDIA. However, in at least one embodiment, one or more servers 1178 may include a deep learning infrastructure in a data center using CPU power.

[0243] In at least one embodiment, the deep learning infrastructure of one or more servers 1178 may be capable of fast, real-time inference and may use this capability to assess and verify the health of the processors, software, and / or associated hardware in vehicle 1100. For example, in at least one embodiment, the deep learning infrastructure may receive periodic updates from vehicle 1100, such as image sequences and / or objects located by vehicle 1100 in the image sequences (e.g., via computer vision and / or other machine learning object classification techniques). In at least one embodiment, the deep learning infrastructure may run its own neural network to identify objects and compare them with objects identified by vehicle 1100, and if the results do not match and the deep learning infrastructure determines that the AI ​​in vehicle 1100 is malfunctioning, one or more servers 1178 may signal to vehicle 1100, instructing vehicle 1100's fail-safe computer to take control, notify passengers, and complete a safe stopping operation.

[0244] In at least one embodiment, one or more servers 1178 may include one or more GPUs 1184 and one or more programmable inference accelerators (e.g., NVIDIA's TensorRT 3 devices). In at least one embodiment, the combination of GPU-driven servers and inference acceleration enables real-time response. In at least one embodiment, servers driven by CPUs, FPGAs, and other processors may be used for inference, such as in situations where performance is less critical. In at least one embodiment, one or more hardware structures 815 are used to execute one or more embodiments. This document incorporates... Figure 8A and / or Figure 8B Provide details about the 815 hardware architecture.

[0245] Computer System

[0246] Figure 12 This is a block diagram illustrating an exemplary computer system according to at least one embodiment. The exemplary computer system may be a system of interconnected devices and components, a system-on-a-chip (SoC), or some combination thereof formed with a processor, which may include an execution unit for executing instructions. In at least one embodiment, according to this disclosure, such as in the embodiments described herein, computer system 1200 may include, but is not limited to, components such as processor 1202 for employing execution units (including logic) to execute algorithms for process data. In at least one embodiment, computer system 1200 may include a processor, such as one available from Intel Corporation of Santa Clara, California. Processor family, Xeon TM , XScale TM and / or StrongARM TM , Core TM or Nervana TM The microprocessor can be used, although other systems (including PCs, engineering workstations, set-top boxes, etc.) with other microprocessors can also be used. In at least one embodiment, the computer system 1200 can execute a version of the Windows operating system available from Microsoft Corporation of Redmond, Washington, although other operating systems (such as UNIX and Linux), embedded software, and / or graphical user interfaces can also be used.

[0247] The embodiments can be used in other devices, such as handheld devices and embedded applications. Some examples of handheld devices include cellular phones, Internet Protocol (IP) devices, digital cameras, personal digital assistants (“PDAs”), and handheld PCs. In at least one embodiment, the embedded application may include a microcontroller, a digital signal processor (“DSP”), a system-on-a-chip (SoC), a network computer (“NetPC”), a set-top box, a network hub, a wide area network (“WAN”) switch, or any other system capable of executing one or more instructions according to at least one embodiment.

[0248] In at least one embodiment, the computer system 1200 may include, but is not limited to, a processor 1202, which may include, but is not limited to, one or more execution units 1208 for performing machine learning model training and / or inference according to the techniques described herein. In at least one embodiment, the computer system 1200 is a single-processor desktop or server system, but in another embodiment, the computer system 1200 may be a multiprocessor system. In at least one embodiment, the processor 1202 may include, but is not limited to, for example, a Complex Instruction Set Computer (“CISC”) microprocessor, a Reduced Instruction Set Computing (“RISC”) microprocessor, a Very Long Instruction Word (“VLIW”) microprocessor, a processor implementing instruction set combination, or any other processor device, such as a digital signal processor. In at least one embodiment, the processor 1202 may be coupled to a processor bus 1210, which can transmit data signals between the processor 1202 and other components in the computer system 1200.

[0249] In at least one embodiment, processor 1202 may include, but is not limited to, a Level 1 (“L1”) internal cache memory (“cache”) 1204. In at least one embodiment, processor 1202 may have a single internal cache or multiple levels of internal caches. In at least one embodiment, the cache memory may reside external to processor 1202. Depending on specific implementation and requirements, other embodiments may also include a combination of internal and external caches. In at least one embodiment, register file 1206 may store different types of data in various registers, including but not limited to integer registers, floating-point registers, status registers, and instruction pointer registers.

[0250] In at least one embodiment, an execution unit 1208, including but not limited to logic for performing integer and floating-point operations, is also located in the processor 1202. In at least one embodiment, the processor 1202 may also include a microcode (“ucode”) read-only memory (“ROM”) storing microcode for certain macro instructions. In at least one embodiment, the execution unit 1208 may include logic for processing a packaged instruction set 1209. In at least one embodiment, by including the packaged instruction set 1209 in the instruction set of the general-purpose processor and the associated circuitry to be executed, operations used by numerous multimedia applications can be performed using packaged data in the processor 1202. In at least one embodiment, numerous multimedia applications can be accelerated and executed more efficiently by performing operations on packaged data using the full width of the processor's data bus, eliminating the need to transfer smaller data units on the processor's data bus to perform one or more operations on one data element at a time.

[0251] In at least one embodiment, execution unit 1208 may also be used in a microcontroller, embedded processor, graphics device, DSP, and other types of logic circuitry. In at least one embodiment, computer system 1200 may include, but is not limited to, memory 1220. In at least one embodiment, memory 1220 may be a dynamic random access memory (“DRAM”) device, a static random access memory (“SRAM”) device, a flash memory device, or other memory device. In at least one embodiment, memory 1220 may store one or more instructions 1219 and / or data 1221 represented by data signals executable by processor 1202.

[0252] In at least one embodiment, the system logic chip may be coupled to the processor bus 1210 and the memory 1220. In at least one embodiment, the system logic chip may include, but is not limited to, a memory controller hub (“MCH”) 1216, and the processor 1202 may communicate with the MCH 1216 via the processor bus 1210. In at least one embodiment, the MCH 1216 may provide a high-bandwidth memory path 1218 to the memory 1220 for storing instructions and data, as well as for storing graphics commands, data, and textures. In at least one embodiment, the MCH 1216 may direct data signals between the processor 1202, the memory 1220, and other components in the computer system 1200, and bridge data signals between the processor bus 1210, the memory 1220, and the system I / O interface 1222. In at least one embodiment, the system logic chip may provide a graphics port for coupling to a graphics controller. In at least one embodiment, MCH 1216 can be coupled to memory 1220 via high-bandwidth memory path 1218, and graphics / video card 1212 can be coupled to MCH 1216 via Accelerated Graphics Port (“AGP”) interconnect 1214.

[0253] In at least one embodiment, the computer system 1200 may use the system I / O interface 1222 as a proprietary hub interface bus to couple the MCH 1216 to the I / O controller hub (“ICH”) 1230. In at least one embodiment, the ICH 1230 may provide direct connectivity to certain I / O devices via a local I / O bus. In at least one embodiment, the local I / O bus may include, but is not limited to, a high-speed I / O bus for connecting peripheral devices to the memory 1220, chipset, and processor 1202. Examples may include, but are not limited to, an audio controller 1229, a firmware hub (“flash BIOS”) 1228, a wireless transceiver 1226, a data storage 1224, a conventional I / O controller 1223 including a user input and keyboard interface 1225, a serial expansion port 1227 (such as a Universal Serial Bus (“USB”) port), and a network controller 1234. In at least one embodiment, the data storage 1224 may include a hard disk drive, a floppy disk drive, a CD-ROM device, a flash memory device, or other mass storage device.

[0254] In at least one embodiment, Figure 12 A system including interconnected hardware devices or "chips" is shown, while in other embodiments, Figure 12 An exemplary SoC can be shown. In at least one embodiment, Figure 12The devices shown can be interconnected using proprietary interconnects, standardized interconnects (e.g., PCIe), or some combination thereof. In at least one embodiment, one or more components of the computer system 1200 are interconnected using a Compute Fast Link (CXL) interconnect.

[0255] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 can be... Figure 12 Used in systems for inference or prediction operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases as described herein.

[0256] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0257] Figure 13 This is a block diagram illustrating an electronic device 1300 for utilizing a processor 1310 according to at least one embodiment. In at least one embodiment, the electronic device 1300 may be, for example, but not limited to, a laptop computer, tower server, rack server, blade server, laptop computer, desktop computer, tablet computer, mobile device, telephone, embedded computer, or any other suitable electronic device.

[0258] In at least one embodiment, the electronic device 1300 may include, but is not limited to, a processor 1310 communicatively coupled to any suitable number or type of components, peripherals, modules, or devices. In at least one embodiment, the processor 1310 is coupled using a bus or interface, such as I... 2 C-bus, System Management Bus (“SMBus”), Low Pin Count (LPC) bus, Serial Peripheral Interface (“SPI”), High Definition Audio (“HDA”) bus, Serial Advanced Technology Accessory (“SATA”) bus, Universal Serial Bus (“USB”) (versions 1, 2, 3, etc.), or Universal Asynchronous Receiver / Transmitter (“UART”) bus. In at least one embodiment, Figure 13 The system shown includes interconnected hardware devices or "chips," while in other embodiments, Figure 13 An exemplary SoC can be shown. In at least one embodiment, Figure 13The devices shown can be interconnected using proprietary interconnects, standardized interconnects (e.g., PCIe), or some combination thereof. In at least one embodiment, Figure 13 One or more components are interconnected using Computational Fast Link (CXL) interconnects.

[0259] In at least one embodiment, Figure 13 It may include a display 1324, a touch screen 1325, a touchpad 1330, a near field communication unit (“NFC”) 1345, a sensor hub 1340, a thermal sensor 1346, a fast chipset (“EC”) 1335, a trusted platform module (“TPM”) 1338, a BIOS / firmware / flash (“BIOS, FW Flash”) 1322, a DSP 1360, a drive 1320 (such as a solid-state drive (“SSD”) or a hard disk drive (“HDD”)), a wireless local area network unit (“WLAN”) 1350, a Bluetooth unit 1352, a wireless wide area network unit (“WWAN”) 1356, a global positioning system (GPS) unit 1355, a camera (“USB 3.0 camera”) 1354 (such as a USB 3.0 camera) and / or a low-power double data rate (“LPDDR”) memory unit (“LPDDR3”) 1315 implemented in, for example, the LPDDR3 standard. These components can each be implemented in any suitable way.

[0260] In at least one embodiment, other components may be communicatively coupled to processor 1310 via the components described herein. In at least one embodiment, accelerometer 1341, ambient light sensor (“ALS”) 1342, compass 1343, and gyroscope 1344 may be communicatively coupled to sensor hub 1340. In at least one embodiment, thermal sensor 1339, fan 1337, keyboard 1336, and touchpad 1330 may be communicatively coupled to EC 1335. In at least one embodiment, speaker 1363, earphone 1364, and microphone (“mic”) 1365 may be communicatively coupled to audio unit (“audio codec and Class D amplifier”) 1362, which in turn may be communicatively coupled to DSP 1360. In at least one embodiment, audio unit 1362 may include, for example, but not limited to, audio encoder / decoder (“codec”) and Class D amplifier. In at least one embodiment, SIM card (“SIM”) 1357 may be communicatively coupled to WWAN unit 1356. In at least one embodiment, components such as WLAN unit 1350, Bluetooth unit 1352, and WWAN unit 1356 may be implemented as next-generation form factor (“NGFF”).

[0261] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 can be... Figure 13 Used in systems for inference or prediction operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases as described herein.

[0262] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0263] Figure 14 A computer system 1400 according to at least one embodiment is shown. In at least one embodiment, the computer system 1400 is configured to implement various processes and methods described throughout this disclosure.

[0264] In at least one embodiment, the computer system 1400 includes, but is not limited to, at least one central processing unit (“CPU”) 1402 connected to a communication bus 1410 implemented using any suitable protocol, such as PCI (“Peripheral Component Interconnect”), Peripheral Component Interconnect Fast (“PCI-Express”), AGP (“Accelerated Graphics Port”), HyperTransport, or any other bus or point-to-point communication protocol. In at least one embodiment, the computer system 1400 includes, but is not limited to, main memory 1404 and control logic (e.g., implemented in hardware, software, or a combination thereof), and data is stored in the main memory 1404, which may take the form of random access memory (“RAM”). In at least one embodiment, a network interface subsystem (“network interface”) 1422 provides an interface to other computing devices and networks for receiving data from and sending data to other systems using the computer system 1400.

[0265] In at least one embodiment, the computer system 1400 includes, but is not limited to, an input device 1408, a parallel processing system 1412, and a display device 1406, which may be implemented using conventional cathode ray tube (“CRT”), liquid crystal display (“LCD”), light-emitting diode (“LED”) display, plasma display, or other suitable display technologies. In at least one embodiment, user input is received from the input device 1408 (such as a keyboard, mouse, touchpad, microphone, etc.). In at least one embodiment, each module described herein may reside on a single semiconductor platform to form the processing system.

[0266] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 can be... Figure 14 The system is used to perform inference or prediction operations based at least in part on weight parameters calculated using the neural network training operations, neural network functions and / or architectures, or neural network use cases described herein.

[0267] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0268] Figure 15 A computer system 1500 according to at least one embodiment is illustrated. In at least one embodiment, the computer system 1500 includes, but is not limited to, a computer 1510 and a USB flash drive 1520. In at least one embodiment, the computer 1510 may include, but is not limited to, any number and type of processors (not shown) and memory (not shown). In at least one embodiment, the computer 1510 includes, but is not limited to, a server, a cloud instance, a laptop computer, and a desktop computer.

[0269] In at least one embodiment, the USB flash drive 1520 includes, but is not limited to, a processing unit 1530, a USB interface 1540, and USB interface logic 1550. In at least one embodiment, the processing unit 1530 can be any instruction execution system, apparatus, or device capable of executing instructions. In at least one embodiment, the processing unit 1530 can include, but is not limited to, any number and type of processing cores (not shown). In at least one embodiment, the processing unit 1530 includes an application-specific integrated circuit (“ASIC”) optimized to perform any number and type of operations associated with machine learning. For example, in at least one embodiment, the processing unit 1530 is a tensor processing unit (“TPC”) optimized to perform machine learning inference operations. In at least one embodiment, the processing unit 1530 is a vision processing unit (“VPU”) optimized to perform machine vision and machine learning inference operations.

[0270] In at least one embodiment, the USB interface 1540 can be any type of USB connector or USB receptacle. For example, in at least one embodiment, the USB interface 1540 is a USB 3.0 Type-C receptacle for data and power. In at least one embodiment, the USB interface 1540 is a USB 3.0 Type-A connector. In at least one embodiment, the USB interface logic 1550 may include any amount and type of logic enabling the processing unit 1530 to interface with a device (e.g., computer 1510) via the USB connector 1540.

[0271] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 can be... Figure 15 Used in systems for inference or prediction operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases as described herein.

[0272] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0273] Figure 16AAn exemplary architecture is illustrated in which multiple GPUs 1610(1)-1610(N) are communicatively coupled to multiple multi-core processors 1605(1)-1605(M) via high-speed links 1640(1)-1640(N) (e.g., bus, point-to-point interconnect, etc.). In at least one embodiment, the high-speed links 1640(1)-1640(N) support communication throughput of 4GB / s, 30GB / s, 80GB / s, or higher. In at least one embodiment, various interconnect protocols may be used, including but not limited to PCIe 4.0 or 5.0 and NVLink 2.0. In the various figures, “N” and “M” represent positive integers, the values ​​of which may vary from figure to figure.

[0274] Furthermore, in at least one embodiment, two or more GPUs 1610 are interconnected via high-speed links 1629(1)-1629(2), which can be implemented using a protocol / link similar to or different from that used for high-speed links 1640(1)-1640(N). Similarly, two or more multi-core processors 1605 can be connected via high-speed link 1628, which can be a symmetric multiprocessor (SMP) bus operating at speeds of 20GB / s, 30GB / s, 120GB / s, or higher. Alternatively, similar protocols / links (e.g., via a common interconnect structure) can be used. Figure 16A This shows all communication between the various system components.

[0275] In at least one embodiment, each multi-core processor 1605 is communicatively coupled to processor memories 1601(1)-1601(M) via memory interconnects 1626(1)-1626(M), and each GPU 1610(1)-1610(N) is communicatively coupled to GPU memories 1620(1)-1620(N) via GPU memory interconnects 1650(1)-1650(N). In at least one embodiment, memory interconnects 1626 and 1650 may utilize similar or different memory access technologies. By way of example and not limitation, processor memories 1601(1)-1601(M) and GPU memories 1620 may be volatile memories, such as dynamic random access memory (DRAM) (including stacked DRAM), graphics DDR SDRAM (GDDR) (e.g., GDDR5, GDDR6), or high bandwidth memory (HBM), and / or may be non-volatile memories, such as 3D XPoint or Nano-RAM. In at least one embodiment, some portions of the processor memory 1601 may be volatile memory, while other portions may be non-volatile memory (e.g., using a two-level memory (2LM) hierarchy).

[0276] As described herein, although the individual multi-core processors 1605 and GPUs 1610 can be physically coupled to specific memories 1601 and 1620 respectively, and / or can implement a unified memory architecture, in which the virtual system address space (also known as the “effective address” space) is distributed among the individual physical memories. For example, processor memories 1601(1)–1601(M) can each include 64 GB of system memory address space, and GPU memories 1620(1)–1620(N) can each include 32 GB of system memory address space, resulting in a total of 256 GB of addressable memory when M = 2 and N = 4. Other values ​​for N and M are possible.

[0277] Figure 16B Additional details are shown regarding the interconnection between a multi-core processor 1607 and a graphics acceleration module 1646 according to an exemplary embodiment. In at least one embodiment, the graphics acceleration module 1646 may include one or more GPU chips integrated on a line card coupled to the processor 1607 via a high-speed link 1640 (e.g., PCIe bus, NVLink, etc.). In at least one embodiment, the graphics acceleration module 1646 may alternatively be integrated on a package or chip having the processor 1607.

[0278] In at least one embodiment, the processor 1607 includes a plurality of cores 1660A-1660D, each core having a translation back cover buffer (“TLB”) 1661A-1661D and one or more caches 1662A-1662D. In at least one embodiment, the cores 1660A-1660D may include various other components, not shown, for executing instructions and processing data. In at least one embodiment, the caches 1662A-1662D may include Level 1 (L1) and Level 2 (L2) caches. Furthermore, one or more shared caches 1656 may be included in the caches 1662A-1662D and shared by the respective groups of cores 1660A-1660D. For example, one embodiment of the processor 1607 includes 24 cores, each core having its own L1 cache, 12 shared L2 caches, and 12 shared L3 caches. In this embodiment, two adjacent cores share one or more L2 and L3 caches. In at least one embodiment, the processor 1607 and the graphics acceleration module 1646 are connected to a system memory 1614, which may include... Figure 16A The processor memory in the memory is 1601(1)-1601(M).

[0279] In at least one embodiment, consistency of data and instructions stored in the various caches 1662A-1662D, 1656 and system memory 1614 is maintained via inter-core communication through the consistency bus 1664. In at least one embodiment, for example, each cache may have associated cache consistency logic / circuit to communicate via the consistency bus 1664 in response to the detection of a read or write to a particular cache line. In at least one embodiment, a cache snooping protocol is implemented via the consistency bus 1664 to snoop on cache accesses.

[0280] In at least one embodiment, proxy circuitry 1625 communicatively couples graphics acceleration module 1646 to coherence bus 1664, thereby allowing graphics acceleration module 1646 to participate in cache coherence protocols as a peer of cores 1660A-1660D. Specifically, in at least one embodiment, interface 1635 provides connectivity to proxy circuitry 1625 via high-speed link 1640, and interface 1637 connects graphics acceleration module 1646 to high-speed link 1640.

[0281] In at least one embodiment, the accelerator integrated circuit 1636 provides cache management, memory access, context management, and interrupt management services for a plurality of graphics processing engines 1631(1)-1631(N) of the graphics acceleration module 1646. In at least one embodiment, the graphics processing engines 1631(1)-1631(N) may each include a separate graphics processing unit (GPU). In at least one embodiment, the graphics processing engines 1631(1)-1631(N) may alternatively include different types of graphics processing engines within the GPU, such as graphics execution units, media processing engines (e.g., video encoders / decoders), samplers, and blit (block-moving) engines. In at least one embodiment, the graphics acceleration module 1646 may be a GPU having a plurality of graphics processing engines 1631(1)-1631(N), or the graphics processing engines 1631(1)-1631(N) may be individual GPUs integrated on a general-purpose package, line card, or chip.

[0282] In at least one embodiment, the accelerator integrated circuit 1636 includes a memory management unit (MMU) 1639 for performing various memory management functions, such as virtual-to-physical memory translation (also known as effective-to-real memory translation), and a memory access protocol for accessing system memory 1614. In at least one embodiment, the MMU 1639 may also include a translation back buffer (“TLB”) (not shown) for caching virtual / effective-to-physical / real address translations. In at least one embodiment, cache 1638 may store commands and data for efficient access by graphics processing engines 1631(1)-1631(N). In at least one embodiment, a fetch unit 1644 may be used to keep data stored in cache 1638 and graphics memory 1633(1)-1633(M) consistent with core caches 1662A-1662D, 1656 and system memory 1614. As previously stated, this can represent cache 1638 and memory 1633(1)-1633(M) being implemented via proxy circuit 1625 (e.g., sending updates related to the modification / access of cache lines on processor caches 1662A-1662D, 1656 to cache 1638 and receiving updates from cache 1638).

[0283] In at least one embodiment, a set of registers 1645 stores context data of threads executed by graphics processing engines 1631(1)-1631(N), and context management circuitry 1648 manages the thread context. For example, context management circuitry 1648 can perform save and restore operations to save and restore the context of individual threads during context switching (e.g., saving the first thread and storing the second thread so that the second thread can be executed by the graphics processing engine). For example, during context switching, context management circuitry 1648 can store the current register value in a designated area of ​​memory (e.g., identified by a context pointer). The register value can then be restored upon returning to the context. In at least one embodiment, interrupt management circuitry 1647 receives and processes interrupts received from system devices.

[0284] In at least one embodiment, MMU 1639 translates virtual / effective addresses from graphics processing engine 1631 into real / physical addresses in system memory 1614. In at least one embodiment, accelerator integrated circuit 1636 supports multiple (e.g., 4, 8, 16) graphics accelerator modules 1646 and / or other accelerator devices. In at least one embodiment, graphics accelerator module 1646 may be dedicated to a single application executing on processor 1607, or may be shared among multiple applications. In at least one embodiment, a virtualized graphics execution environment is presented, wherein resources of graphics processing engines 1631(1)-1631(N) are shared with multiple applications or virtual machines (VMs). In at least one embodiment, resources may be subdivided into “slices” based on processing requirements and priorities associated with VMs and / or applications, which are allocated to different VMs and / or applications.

[0285] In at least one embodiment, the accelerator integrated circuit 1636 acts as a bridge to the system of the graphics acceleration module 1646 and provides address translation and system memory caching services. Additionally, in at least one embodiment, the accelerator integrated circuit 1636 can provide virtualization facilities for the host processor to manage the virtualization, interrupt, and memory management of the graphics processing engines 1631(1)-1631(N).

[0286] In at least one embodiment, since the hardware resources of the graphics processing engines 1631(1)-1631(N) are explicitly mapped to the real address space seen by the host processor 1607, any host processor can directly address these resources using valid address values. In at least one embodiment, a function of the accelerator integrated circuit 1636 is the physical separation of the graphics processing engines 1631(1)-1631(N), making them appear as independent units to the system.

[0287] In at least one embodiment, one or more graphics memories 1633(1)-1633(M) are coupled to each graphics processing engine 1631(1)-1631(N), where N = M. In at least one embodiment, the graphics memories 1633(1)-1633(M) store instructions and data being processed by each graphics processing engine 1631(1)-1631(N). In at least one embodiment, the graphics memories 1633(1)-1633(M) may be volatile memories, such as DRAM (including stacked DRAM), GDDR memory (e.g., GDDR5, GDDR6), or HBM, and / or may be non-volatile memories, such as 3D XPoint or Nano-RAM.

[0288] In at least one embodiment, to reduce data traffic on the high-speed link 1640, a biasing technique can be used to ensure that the data stored in the graphics memory 1633(1)-1633(M) is the data most frequently used by the graphics processing engine 1631(1)-1631(N), and preferably data that the cores 1660A-1660D do not use (or at least not frequently use). Similarly, in at least one embodiment, the biasing mechanism attempts to keep data needed by the cores (and preferably not needed by the graphics processing engine 1631(1)-1631(N)) in the caches 1662A-1662D, 1656 and system memory 1614.

[0289] Figure 16C Another exemplary embodiment is shown, wherein the accelerator integrated circuit 1636 is integrated within the processor 1607. In this embodiment, the graphics processing engines 1631(1)-1631(N) communicate directly with the accelerator integrated circuit 1636 via a high-speed link 1640 through interfaces 1637 and 1635 (again, which can be any form of bus or interface protocol). In at least one embodiment, the accelerator integrated circuit 1636 can perform operations related to... Figure 16B The described operation is similar, but due to its close proximity to the coherence bus 1664 and caches 1662A-1662D, 1656, it may have higher throughput. In at least one embodiment, the accelerator integrated circuit supports different programming models, including a process-specific programming model (without graphics acceleration module virtualization) and a shared programming model (with virtualization), which may include a programming model controlled by the accelerator integrated circuit 1636 and a programming model controlled by the graphics acceleration module 1646.

[0290] In at least one embodiment, graphics processing engines 1631(1)-1631(N) are dedicated to a single application or process under a single operating system. In at least one embodiment, a single application can funnel requests from other applications to graphics processing engines 1631(1)-1631(N), thereby providing virtualization within a VM / partition.

[0291] In at least one embodiment, graphics processing engines 1631(1)-1631(N) can be shared by multiple VM / application partitions. In at least one embodiment, the shared model can use a hypervisor to virtualize graphics processing engines 1631(1)-1631(N) to allow access by each operating system. In at least one embodiment, for a single-partition system without a hypervisor, the operating system owns graphics processing engines 1631(1)-1631(N). In at least one embodiment, the operating system can virtualize graphics processing engines 1631(1)-1631(N) to provide access to each process or application.

[0292] In at least one embodiment, the graphics acceleration module 1646 or the individual graphics processing engine 1631(1)-1631(N) uses a process handle to select a process element. In at least one embodiment, the process element is stored in system memory 1614 and can be addressed using the effective address to real address translation techniques described herein. In at least one embodiment, the process handle may be an implementation-specific value provided to the host process when registering its context with the graphics processing engine 1631(1)-1631(N) (i.e., invoking system software to add the process element to the process element linked list). In at least one embodiment, the lower 16 bits of the process handle may be the offset of the process element in the process element linked list.

[0293] Figure 16D An exemplary accelerator integration slice 1690 is illustrated. In at least one embodiment, a "slice" includes a designated portion of the processing resources of an accelerator integrated circuit 1636. In at least one embodiment, the application is an effective address space 1682 in system memory 1614, which stores process element 1683. In at least one embodiment, process element 1683 is stored in response to a GPU call 1681 from an application 1680 executing on processor 1607. In at least one embodiment, process element 1683 contains the process state of the corresponding application 1680. In at least one embodiment, a job descriptor (WD) 1684 contained in process element 1683 may be a single job requested by the application, or it may contain a pointer to a job queue. In at least one embodiment, WD 1684 is a pointer to a job request queue in the effective address space 1682 of the application.

[0294] In at least one embodiment, the graphics acceleration module 1646 and / or the various graphics processing engines 1631(1)-1631(N) may be shared by all processes or subsets of processes in the system. In at least one embodiment, infrastructure may be included for setting process states and sending WD 1684 to the graphics acceleration module 1646 to begin operations in a virtualized environment.

[0295] In at least one embodiment, the process-specific programming model is implementation-specific. In at least one embodiment, in this model, a single process owns either the graphics acceleration module 1646 or an individual graphics processing engine 1631. In at least one embodiment, when the graphics acceleration module 1646 is owned by a single process, the hypervisor initializes the accelerator integrated circuit for the owned partition, and when the graphics acceleration module 1646 is assigned, the operating system initializes the accelerator integrated circuit 1636 for the owned process.

[0296] In at least one embodiment, during operation, the WD acquisition unit 1691 in the accelerator integration slice 1690 acquires the next WD 1684, which includes instructions for work to be performed by one or more graphics processing engines of the graphics acceleration module 1646. In at least one embodiment, data from the WD 1684 may be stored in register 1645 and used by the MMU 1639, interrupt management circuitry 1647, and / or context management circuitry 1648, as shown. For example, one embodiment of the MMU 1639 includes segment / page walk circuitry for accessing segment / page tables 1686 within the OS virtual address space 1685. In at least one embodiment, the interrupt management circuitry 1647 may process an interrupt event 1692 received from the graphics acceleration module 1646. In at least one embodiment, when performing graphics operations, a valid address 1693 generated by graphics processing engines 1631(1)-1631(N) is translated into a real address by the MMU 1639.

[0297] In at least one embodiment, register 1645 is copied for each graphics processing engine 1631(1)-1631(N) and / or graphics acceleration module 1646, and register 1645 may be initialized by a hypervisor or operating system. In at least one embodiment, each of these copied registers may be included in accelerator integration slice 1690. Exemplary registers that may be initialized by a hypervisor are shown in Table 1.

[0298] Table 1 – Registers for Supervisor Initialization

[0299]

[0300] Table 2 shows exemplary registers that can be initialized by the operating system.

[0301] Table 2 – Operating System Initialization Registers

[0302]

[0303] In at least one embodiment, each WD 1684 is specific to a particular graphics acceleration module 1646 and / or graphics processing engine 1631(1)-1631(N). In at least one embodiment, it contains all the information required for the graphics processing engine 1631(1)-1631(N) to complete its work, or it may be a pointer to a memory location where the application has set up a command queue for the work to be completed.

[0304] Figure 16E Additional details of an exemplary embodiment of the shared model are shown. This embodiment includes a hypervisor real address space 1698, in which a list of process elements 1699 is stored. In at least one embodiment, the hypervisor real address space 1698 can be accessed via a hypervisor 1696, which virtualizes the graphics acceleration module engine for operating system 1695.

[0305] In at least one embodiment, the shared programming model allows all processes or subsets of processes from all partitions or subsets of partitions in the system to use the graphics acceleration module 1646. In at least one embodiment, there are two programming models in which the graphics acceleration module 1646 is shared by multiple processes and partitions, namely time-slice sharing and graphics-oriented sharing.

[0306] In at least one embodiment, in this model, the hypervisor 1696 owns the graphics acceleration module 1646 and makes its functionality available to all operating systems 1695. In at least one embodiment, for the graphics acceleration module 1646 to support virtualization through the hypervisor 1696, the graphics acceleration module 1646 may comply with certain requirements, such as (1) the job requests of the application must be autonomous (i.e., no state needs to be maintained between jobs), or the graphics acceleration module 1646 must provide a context saving and recovery mechanism, (2) the graphics acceleration module 1646 guarantees that the job requests of the application are completed within a specified amount of time, including any conversion errors, or the graphics acceleration module 1646 provides the ability to preempt job processing, and (3) when operating in a directed shared programming model, the fairness of the graphics acceleration module 1646 among processes must be ensured.

[0307] In at least one embodiment, application 1680 needs to make operating system 1695 system calls using the graphics acceleration module type, working descriptor (WD), permission mask register (AMR) value, and context save / restore region pointer (CSRP). In at least one embodiment, the graphics acceleration module type describes the target acceleration function for the system call. In at least one embodiment, the graphics acceleration module type can be a system-specific value. In at least one embodiment, the WD is specifically formatted for graphics acceleration module 1646 and can take the form of graphics acceleration module 1646 commands, valid address pointers to user-defined structures, valid address pointers to command queues, or any other data structure describing the work to be performed by graphics acceleration module 1646.

[0308] In at least one embodiment, the AMR value is the AMR state for the current process. In at least one embodiment, the value passed to the operating system is similar to that of the application that sets the AMR. In at least one embodiment, if the implementation of the accelerator integrated circuit 1636 (not shown) and the graphics acceleration module 1646 does not support the User Rights Mask Overwrite Register (UAMOR), the operating system may apply the current UAMOR value to the AMR value before passing the AMR in the hypervisor call. In at least one embodiment, the hypervisor 1696 may selectively apply the current Rights Mask Overwrite Register (AMOR) value before placing the AMR into the process element 1683. In at least one embodiment, CSRP is one of the registers 1645 that contains the effective address of a region in the effective address space 1682 of the application for the graphics acceleration module 1646 to save and restore the context state. In at least one embodiment, this pointer is optional if it is not necessary to save state between jobs or when a job is preempted. In at least one embodiment, the context save / restore region may be fixed system memory.

[0309] Upon receiving a system call, the operating system 1695 can verify that the application 1680 has been registered and granted permission to use the graphics acceleration module 1646. Then, in at least one embodiment, the operating system 1695 uses the information shown in Table 3 to invoke the hypervisor 1696.

[0310] Table 3 – Operating System to Hypervisor Call Parameters

[0311]

[0312] In at least one embodiment, upon receiving a hypervisor call, the hypervisor 1696 verifies that the operating system 1695 has been registered and granted permission to use the graphics acceleration module 1646. Then, in at least one embodiment, the hypervisor 1696 adds the process element 1683 to a linked list of process elements of the corresponding graphics acceleration module 1646 type. In at least one embodiment, the process element may include the information shown in Table 4.

[0313] Table 4 – Process Element Information

[0314]

[0315]

[0316] In at least one embodiment, the hypervisor initializes multiple accelerator integration slice 1690 registers 1645.

[0317] like Figure 16F As shown, in at least one embodiment, a unified memory is used, which is addressable via a common virtual memory address space for accessing physical processor memories 1601(1)-1601(N) and GPU memories 1620(1)-1620(N). In this implementation, operations performed on GPUs 1610(1)-1610(N) utilize the same virtual / effective memory address space to access processor memories 1601(1)-1601(M) and vice versa, thereby simplifying programmability. In at least one embodiment, a first portion of the virtual / effective address space is allocated to processor memory 1601(1), a second portion to second processor memory 1601(N), a third portion to GPU memory 1620(1), and so on. In at least one embodiment, the entire virtual / effective memory space (sometimes referred to as the effective address space) is thus distributed across each of processor memories 1601 and GPU memories 1620, thereby allowing any processor or GPU to access that memory using a virtual address mapped to any physical memory.

[0318] In at least one embodiment, the bias / coherence management circuitry 1694A-1694E within one or more MMUs 1639A-1639E ensures cache coherence between one or more host processors (e.g., 1605) and the cache of the GPU 1610, and implements biasing techniques to indicate the physical memory in which certain types of data should be stored. In at least one embodiment, although in Figure 16FSeveral instances of the bias / coherence management circuitry 1694A-1694E are shown, but the bias / coherence circuitry can be implemented within the MMU of one or more host processors 1605 and / or within the accelerator integrated circuit 1636.

[0319] One embodiment allows GPU memory 1620 to be mapped as part of system memory and accessed using shared virtual memory (SVM) technology without suffering the performance drawbacks associated with system-wide cache coherence. In at least one embodiment, the ability of GPU memory 1620 to be accessed as system memory without the heavy overhead of cache coherence provides a favorable operating environment for GPU offloading. In at least one embodiment, this arrangement allows the host processor 1605 to software-set operands and access computation results without the overhead of conventional I / O DMA data copying. In at least one embodiment, such conventional copying includes driver calls, interrupts, and memory-mapped I / O (MMIO) accesses, all of which are less efficient than simple memory accesses. In at least one embodiment, the ability to access GPU memory 1620 without cache coherence overhead can be critical to the execution time of offloaded computations. In at least one embodiment, for example, in cases with high streaming write memory traffic, cache coherence overhead can significantly reduce the effective write bandwidth seen by GPU 1610. In at least one embodiment, the efficiency of operand setting, the efficiency of result access, and the efficiency of GPU computation can play a role in determining the effectiveness of GPU offloading.

[0320] In at least one embodiment, the selection of GPU bias and host processor bias is driven by a bias tracker data structure. In at least one embodiment, for example, a bias table can be used, which may be a page-granular structure (e.g., controlled at the memory page level) comprising 1 or 2 bits of memory pages attached per GPU. In at least one embodiment, with or without a bias cache (e.g., for caching frequently / recently used entries in the bias table) in the GPU 1610, the bias table can be implemented over one or more stolen memory ranges of the GPU memory 1620. Alternatively, in at least one embodiment, the entire bias table can be maintained within the GPU.

[0321] In at least one embodiment, prior to actual access to GPU memory, an access to the bias table entry associated with each access to GPU-attached memory 1620 is performed, resulting in the following operations: In at least one embodiment, a local request from GPU 1610 to find its page in the GPU bias is forwarded directly to the corresponding GPU memory 1620. In at least one embodiment, a local request from the GPU to find its page in the host bias is forwarded to processor 1605 (e.g., via the high-speed link described herein). In at least one embodiment, a request from processor 1605 to find the requested page in the host processor bias completes a request similar to a normal memory read. Alternatively, a request for a page pointing to the GPU bias can be forwarded to GPU 1610. In at least one embodiment, if the GPU is not currently using the page, the GPU can migrate the page to the host processor bias. In at least one embodiment, the page bias state can be changed by a software-based mechanism, a hardware-assisted software mechanism, or, in a limited set of cases, by a purely hardware-based mechanism.

[0322] In at least one embodiment, a mechanism for changing the bias state employs an API call (e.g., OpenCL), which in turn invokes the GPU's device driver, which in turn sends a message (or enqueues a command descriptor) to the GPU, instructing the GPU to change the bias state and, in some migration, performs a cache refresh operation on the host. In at least one embodiment, the cache refresh operation is used for migrating from the host processor 1605 bias to the GPU bias, but not for the reverse migration.

[0323] In at least one embodiment, cache coherence is maintained by temporarily rendering GPU bias pages that the host processor 1605 cannot cache. In at least one embodiment, to access these pages, the processor 1605 may request access from the GPU 1610, which may or may not immediately grant access. Therefore, in at least one embodiment, to reduce communication between the processor 1605 and the GPU 1610, it is beneficial to ensure that the GPU bias pages are pages needed by the GPU rather than those needed by the host processor 1605, and vice versa.

[0324] One or more hardware structures 815 are used to execute one or more embodiments. This document may combine... Figure 8A and / or Figure 8B Provide details about one or more hardware architectures 815.

[0325] Figure 17Exemplary integrated circuits and associated graphics processors according to various embodiments described herein are illustrated, which can be manufactured using one or more IP cores. In addition to those illustrated, at least one embodiment may also include other logic and circuitry, including additional graphics processors / cores, peripheral interface controllers, or general-purpose processor cores.

[0326] Figure 17 This is a block diagram illustrating an exemplary system on a chip integrated circuit 1700 that can be fabricated using one or more IP cores according to at least one embodiment. In at least one embodiment, the integrated circuit 1700 includes one or more application processors 1705 (e.g., CPUs), at least one graphics processor 1710, and may additionally include an image processor 1715 and / or a video processor 1720, any of which may be a modular IP core. In at least one embodiment, the integrated circuit 1700 includes peripheral or bus logic, which includes a USB controller 1725, a UART controller 1730, an SPI / SDIO controller 1735, and an I... 2 S / I 2 C controller 1740. In at least one embodiment, integrated circuit 1700 may include a display device 1745 coupled to one or more of a High Definition Multimedia Interface (HDMI) controller 1750 and a Mobile Industrial Processor Interface (MIPI) display interface 1755. In at least one embodiment, storage may be provided by a flash memory subsystem 1760, which includes flash memory and a flash memory controller. In at least one embodiment, a memory interface may be provided via a memory controller 1765 for accessing an SDRAM or SRAM memory device. In at least one embodiment, some integrated circuits also include an embedded security engine 1770.

[0327] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 may be used in integrated circuit 1700 to infer or predict operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases described herein.

[0328] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0329] Figures 18A-18B Exemplary integrated circuits and associated graphics processors according to various embodiments described herein are illustrated, which may be fabricated using one or more IP cores. In addition to those illustrated, at least one embodiment may include other logic and circuitry, including additional graphics processors / cores, peripheral interface controllers, or general-purpose processor cores.

[0330] Figures 18A-18B This is a block diagram illustrating an exemplary graphics processor used within a SoC according to embodiments described herein. Figure 18A An exemplary graphics processor 1810 of a system-on-a-chip integrated circuit according to at least one embodiment is shown, which can be manufactured using one or more IP cores. Figure 18B An additional exemplary graphics processor 1840 of a system-on-a-chip integrated circuit according to at least one embodiment is shown, which can be manufactured using one or more IP cores. In at least one embodiment, Figure 18A The graphics processor 1810 is a low-power graphics processor core. In at least one embodiment, Figure 18B The graphics processor 1840 is a higher-performance graphics processor core. In at least one embodiment, each graphics processor 1810, 1840 may be... Figure 17 A variant of the 1710 graphics processor.

[0331] In at least one embodiment, the graphics processor 1810 includes a vertex processor 1805 and one or more fragment processors 1815A-1815N (e.g., 1815A, 1815B, 1815C, 1815D to 1815N-1 and 1815N). In at least one embodiment, the graphics processor 1810 may execute different shader programs via separate logic, such that the vertex processor 1805 is optimized to perform operations for vertex shader programs, while one or more fragment processors 1815A-1815N perform fragment (e.g., pixel) shading operations for fragment or pixel shader programs. In at least one embodiment, the vertex processor 1805 performs the vertex processing stage of the 3D graphics pipeline and generates primitive and vertex data. In at least one embodiment, one or more fragment processors 1815A-1815N use the primitive and vertex data generated by the vertex processor 1805 to generate framebuffers for display on a display device. In at least one embodiment, one or more fragment processors 1815A-1815N are optimized to execute fragment shader programs as provided in the OpenGL API, which can be used to perform operations similar to those of pixel shader programs provided in the Direct 3D API.

[0332] In at least one embodiment, the graphics processor 1810 additionally includes one or more memory management units (MMUs) 1820A-1820B, one or more caches 1825A-1825B, and one or more circuit interconnects 1830A-1830B. In at least one embodiment, the one or more MMUs 1820A-1820B provide virtual-to-physical address mapping for the graphics processor 1810 (including for the vertex processor 1805 and / or fragment processors 1815A-1815N), and may reference vertex or image / texture data stored in memory in addition to vertex or image / texture data stored in the one or more caches 1825A-1825B. In at least one embodiment, the one or more MMUs 1820A-1820B may be synchronized with other MMUs within the system, including with... Figure 17 One or more application processors 1705, graphics processors 1715, and / or video processors 1720 are associated with one or more MMUs, such that each processor 1705-1720 can participate in a shared or unified virtual memory system. In at least one embodiment, one or more circuit interconnects 1830A-1830B enable the graphics processor 1810 to interface with other IP cores within the SoC via the SoC's internal bus or via a direct connection.

[0333] In at least one embodiment, the graphics processor 1840 includes, as shown below: Figure 18B The one or more shader cores 1855A-1855N (e.g., 1855A, 1855B, 1855C, 1855D, 1855E, 1855F to 1855N-1 and 1855N) shown provide a unified shader core architecture, wherein a single core or type or core can execute all types of programmable shader code, including shader program code for implementing vertex shaders, fragment shaders, and / or compute shaders. In at least one embodiment, the number of shader cores can vary. In at least one embodiment, the graphics processor 1840 includes an inter-core task manager 1845, which acts as a thread dispatcher for dispatching execution threads to one or more shader cores 1855A-1855N and a tile unit 1858 to accelerate tile-based rendering operations, where rendering operations of the scene are subdivided in the image space, for example, to utilize local spatial consistency within the scene or optimize the use of internal caches.

[0334] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8BDetails are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 can be... Figure 18A and / or Figure 18B The integrated circuits are used to perform inference or prediction operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures, or neural network use cases as described herein.

[0335] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0336] Figures 19A-19B Additional exemplary graphics processor logic according to embodiments described herein is illustrated. In at least one embodiment, Figure 19A It shows that it can be included in Figure 17 The graphics core 1900 within the graphics processor 1710, and in at least one embodiment, may be as follows: Figure 18B The Unified Shader Core 1855A-1855N is shown. Figure 19B A highly parallel general-purpose graphics processing unit (“GPGPU”) 1930 suitable for deployment on a multi-chip module is shown in at least one embodiment.

[0337] In at least one embodiment, the graphics core 1900 includes a shared instruction cache 1902, texture units 1918, and cache / shared memory 1920, which are shared for execution resources within the graphics core 1900. In at least one embodiment, the graphics core 1900 may include multiple slices 1901A-1901N or partitions of each core, and the graphics processor may include multiple instances of the graphics core 1900. In at least one embodiment, slices 1901A-1901N may include supporting logic including local instruction caches 1904A-1904N, thread schedulers 1906A-1906N, thread dispatchers 1908A-1908N, and a set of registers 1910A-1910N. In at least one embodiment, slices 1901A-1901N may include a set of additional functional units (AFU 1912A-1912N), floating-point units (FPU 1914A-1914N), integer arithmetic logic units (ALU 1916A-1916N), address calculation units (ACU 1913A-1913N), double-precision floating-point units (DPFPU1915A-1915N), and matrix processing units (MPU 1917A-1917N).

[0338] In at least one embodiment, the FPU 1914A-1914N can perform single-precision (32-bit) and half-precision (16-bit) floating-point operations, while the DPFPU 1915A-1915N performs double-precision (64-bit) floating-point operations. In at least one embodiment, the ALU1916A-1916N can perform variable-precision integer operations with 8-bit, 16-bit, and 32-bit precision, and can be configured for mixed-precision operations. In at least one embodiment, the MPU 1917A-1917N can also be configured for mixed-precision matrix operations, including half-precision floating-point operations and 8-bit integer operations. In at least one embodiment, the MPU 1917A-1917N can perform various matrix operations to accelerate machine learning application frameworks, including enabling support for accelerated generalized matrix-to-matrix multiplication (GEMM). In at least one embodiment, the AFU1912A-1912N can perform additional logical operations not supported by the floating-point or integer units, including trigonometric function operations (e.g., sine, cosine, etc.).

[0339] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details are provided regarding inference and / or training logic 815. In at least one embodiment, inference and / or training logic 815 may be used in graphics core 1900 for performing inference or prediction operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases as described herein.

[0340] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0341] Figure 19BA general-purpose processing unit (GPGPU) 1930 is illustrated in at least one embodiment, which can be configured to enable highly parallel computational operations to be performed by an array of graphics processing units. In at least one embodiment, the GPGPU 1930 can be directly linked to other instances of the GPGPU 1930 to create a multi-GPU cluster to improve the training speed for deep neural networks. In at least one embodiment, the GPGPU 1930 includes a host interface 1932 for establishing a connection with a host processor. In at least one embodiment, the host interface 1932 is a PCI Express interface. In at least one embodiment, the host interface 1932 can be a vendor-specific communication interface or communication structure. In at least one embodiment, the GPGPU 1930 receives commands from the host processor and uses a global scheduler 1934 to allocate execution threads associated with those commands to a group of computing clusters 1936A-1936H. In at least one embodiment, the computing clusters 1936A-1936H share a cache memory 1938. In at least one embodiment, cache memory 1938 can be used as a higher-level cache than cache memory within computing clusters 1936A-1936H.

[0342] In at least one embodiment, the GPGPU 1930 includes memories 1944A-1944B coupled to computing clusters 1936A-1936H via a set of memory controllers 1942A-1942B. In at least one embodiment, memories 1944A-1944B may include various types of memory devices, including dynamic random access memory (DRAM) or graphics random access memory, such as synchronous graphics random access memory (SGRAM), which includes graphics double data rate (GDDR) memory.

[0343] In at least one embodiment, each of the computing clusters 1936A-1936H includes a set of graphics cores, such as Figure 19A The graphics core 1900 may include various types of integer and floating-point logic units that can perform computational operations over a range of precision suitable for machine learning computations. For example, in at least one embodiment, at least a subset of the floating-point units in each computing cluster 1936A-1936H may be configured to perform 16-bit or 32-bit floating-point operations, while different subsets of the floating-point units may be configured to perform 64-bit floating-point operations.

[0344] In at least one embodiment, multiple instances of GPGPU 1930 can be configured to operate as a computing cluster. In at least one embodiment, the communication used for synchronization and data exchange by computing clusters 1936A-1936H varies between embodiments. In at least one embodiment, multiple instances of GPGPU 1930 communicate via host interface 1932. In at least one embodiment, GPGPU 1930 includes an I / O hub 1939 that couples GPGPU 1930 to GPU link 1940, which provides direct connectivity to other instances of GPGPU 1930. In at least one embodiment, GPU link 1940 is coupled to a dedicated GPU-to-GPU bridge that enables communication and synchronization between multiple instances of GPGPU 1930. In at least one embodiment, GPU link 1940 is coupled to a high-speed interconnect for sending and receiving data to and from other GPGPUs or parallel processors. In at least one embodiment, multiple instances of GPGPU 1930 reside in a separate data processing system and communicate via a network device accessible via host interface 1932. In at least one embodiment, in addition to or as an alternative to host interface 1932, GPU link 1940 may also be configured to implement a connection to the host processor.

[0345] In at least one embodiment, the GPGPU 1930 can be configured to train a neural network. In at least one embodiment, the GPGPU 1930 can be used within an inference platform. In at least one embodiment, when using the GPGPU 1930 for inference, the GPGPU 1930 may include fewer compute clusters 1936A-1936H compared to when using the GPGPU 1930 to train a neural network. In at least one embodiment, the memory technology associated with the memories 1944A-1944B can differ between inference and training configurations, with higher bandwidth memory technology dedicated to the training configuration. In at least one embodiment, the inference configuration of the GPGPU 1930 can support inference-specific instructions. For example, in at least one embodiment, the inference configuration can provide support for one or more 8-bit integer dot product instructions, which can be used during the inference operation of the deployed neural network.

[0346] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8BDetails are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 may be used in the GPGPU 1930 for performing inference or prediction operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases described herein.

[0347] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0348] Figure 20 This is a block diagram illustrating a computing system 2000 according to at least one embodiment. In at least one embodiment, the computing system 2000 includes a processing subsystem 2001 having one or more processors 2002 and a system memory 2004 communicating via an interconnect path that may include a memory hub 2005. In at least one embodiment, the memory hub 2005 may be a separate component within a chipset assembly or may be integrated within one or more processors 2002. In at least one embodiment, the memory hub 2005 is coupled to an I / O subsystem 2011 via a communication link 2006. In at least one embodiment, the I / O subsystem 2011 includes an I / O hub 2007 that enables the computing system 2000 to receive input from one or more input devices 2008. In at least one embodiment, the I / O hub 2007 enables a display controller to provide output to one or more display devices 2010A, the display controller being included in one or more processors 2002. In at least one embodiment, one or more display devices 2010A coupled to the I / O hub 2007 may include local, internal or embedded display devices.

[0349] In at least one embodiment, the processing subsystem 2001 includes one or more parallel processors 2012 coupled to the memory hub 2005 via a bus or other communication link 2013. In at least one embodiment, the communication link 2013 may use one of any number of standards based on a communication link technology or protocol (such as, but not limited to, PCI Express), or may be a vendor-specific communication interface or communication architecture. In at least one embodiment, one or more parallel processors 2012 form a computationally concentrated parallel or vector processing system, which may include a large number of processing cores and / or processing clusters, such as integrated many-core (MIC) processors. In at least one embodiment, some or all of the parallel processors 2012 form a graphics processing subsystem that can output pixels to one or more display devices 2010A coupled via an I / O hub 2007. In at least one embodiment, one or more parallel processors 2012 may also include a display controller and a display interface (not shown) for implementing direct connections to one or more display devices 2010B.

[0350] In at least one embodiment, system storage unit 2014 may be connected to I / O hub 2007 to provide a storage mechanism for computing system 2000. In at least one embodiment, I / O switch 2016 may be used to provide an interface mechanism for enabling connectivity between I / O hub 2007 and other components, such as network adapter 2018 and / or wireless network adapter 2019 integrated into the platform, and various other devices that can be added via one or more additional devices 2020. In at least one embodiment, network adapter 2018 may be an Ethernet adapter or another wired network adapter. In at least one embodiment, wireless network adapter 2019 may include one or more of Wi-Fi, Bluetooth, near field communication (NFC), or other network devices including one or more wireless devices.

[0351] In at least one embodiment, the computing system 2000 may include other components, not explicitly shown, that may also be connected to the I / O hub 2007, including USB or other port connections, optical storage drives, video capture devices, etc. In at least one embodiment, the interconnect can be implemented using any suitable protocol, such as a PCI (Peripheral Component Interconnect) based protocol (e.g., PCI-Express) or other bus or point-to-point communication interface and / or protocol (e.g., NV-Link high-speed interconnect or interconnect protocols). Figure 20 The communication paths of each component.

[0352] In at least one embodiment, one or more parallel processors 2012 include circuitry optimized for graphics and video processing, including, for example, video output circuitry, and constituting a graphics processing unit (GPU). In at least one embodiment, one or more parallel processors 2012 include circuitry optimized for general-purpose processing. In at least one embodiment, components of the computing system 2000 may be integrated with one or more other system elements on a single integrated circuit. For example, in at least one embodiment, one or more parallel processors 2012, a memory hub 2005, one or more processors 2002, and an I / O hub 2007 may be integrated into a system-on-a-chip (SoC) integrated circuit. In at least one embodiment, components of the computing system 2000 may be integrated into a single package to form a system-in-package (SIP) configuration. In at least one embodiment, at least a portion of the components of the computing system 2000 may be integrated into a multi-chip module (MCM), which may interconnect with other MCMs to a modular computing system.

[0353] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 can be... Figure 20 The system is used in 2000 for inference or prediction operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases as described herein.

[0354] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0355] processor

[0356] Figure 21A A parallel processor 2100 according to at least one embodiment is illustrated. In at least one embodiment, the various components of the parallel processor 2100 may be implemented using one or more integrated circuit devices, such as programmable processors, application-specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs). In at least one embodiment, the illustrated parallel processor 2100 is according to an exemplary embodiment. Figure 20 The variant of the 2012 with one or more parallel processors is shown.

[0357] In at least one embodiment, the parallel processor 2100 includes a parallel processing unit 2102. In at least one embodiment, the parallel processing unit 2102 includes an I / O unit 2104 that enables communication with other devices, including other instances of the parallel processing unit 2102. In at least one embodiment, the I / O unit 2104 can be directly connected to other devices. In at least one embodiment, the I / O unit 2104 is connected to other devices via a hub or switch interface (e.g., a memory hub 2105). In at least one embodiment, the connection between the memory hub 2105 and the I / O unit 2104 forms a communication link 2113. In at least one embodiment, the I / O unit 2104 is connected to a host interface 2106 and a memory crossbar switch 2116, wherein the host interface 2106 receives commands for performing processing operations, and the memory crossbar switch 2116 receives commands for performing memory operations.

[0358] In at least one embodiment, when host interface 2106 receives a command buffer via I / O unit 2104, host interface 2106 can route work operations for executing those commands to front end 2108. In at least one embodiment, front end 2108 is coupled to scheduler 2110, which is configured to assign commands or other work items to processing cluster array 2112. In at least one embodiment, scheduler 2110 ensures that processing cluster array 2112 is correctly configured and in an active state before assigning tasks to clusters in processing cluster array 2112. In at least one embodiment, scheduler 2110 is implemented via firmware logic executed on a microcontroller. In at least one embodiment, the microcontroller-implemented scheduler 2110 can be configured to perform complex scheduling and work assignment operations at both coarse and fine granular levels, thereby enabling fast preemption and context switching of threads executing on processing cluster array 2112. In at least one embodiment, host software can demonstrate workloads for scheduling on processing cluster array 2112 via one of multiple graphics processing paths. In at least one embodiment, the workload can then be automatically distributed on the processing cluster array 2112 by the scheduler 2110 logic within the microcontroller, which includes the scheduler 2110.

[0359] In at least one embodiment, the processing cluster array 2112 may include up to "N" processing clusters (e.g., clusters 2114A, 2114B to 2114N), where "N" represents a positive integer (which may be an integer "N" different from the integers used in other diagrams). In at least one embodiment, each cluster 2114A-2114N of the processing cluster array 2112 may execute a large number of concurrent threads. In at least one embodiment, the scheduler 2110 may use various scheduling and / or work allocation algorithms to allocate work to clusters 2114A-2114N in the processing cluster array 2112, which may vary depending on the workload generated for each type of program or computation. In at least one embodiment, scheduling may be handled dynamically by the scheduler 2110, or may be partially assisted by compiler logic during the compilation of program logic configured to be executed by the processing cluster array 2112. In at least one embodiment, different clusters 2114A-2114N in the processing cluster array 2112 may be assigned to process different types of programs or to perform different types of computations.

[0360] In at least one embodiment, the processing cluster array 2112 can be configured to perform various types of parallel processing operations. In at least one embodiment, the processing cluster array 2112 is configured to perform general-purpose parallel computing operations. For example, in at least one embodiment, the processing cluster array 2112 may include logic for performing processing tasks, including filtering video and / or audio data, performing modeling operations, including physical operations, and performing data transformations.

[0361] In at least one embodiment, the processing cluster array 2112 is configured to perform parallel graphics processing operations. In at least one embodiment, the processing cluster array 2112 may include additional logic for supporting the execution of such graphics processing operations, including but not limited to texture sampling logic for performing texture operations, as well as tessellation logic and other vertex processing logic. In at least one embodiment, the processing cluster array 2112 may be configured to execute shader programs related to graphics processing, such as, but not limited to, vertex shaders, tessellation shaders, geometry shaders, and pixel shaders. In at least one embodiment, the parallel processing unit 2102 may transfer data from system memory via I / O unit 2104 for processing. In at least one embodiment, during processing, the transferred data may be stored in on-chip memory (e.g., parallel processor memory 2122) and then written back to system memory.

[0362] In at least one embodiment, when the parallel processing unit 2102 is used to perform graphics processing, the scheduler 2110 can be configured to divide the processing workload into tasks of approximately equal size to better distribute graphics processing operations among multiple clusters 2114A-2114N in the processing cluster array 2112. In at least one embodiment, portions of the processing cluster array 2112 can be configured to perform different types of processing. For example, in at least one embodiment, a first portion can be configured to perform vertex shading and topology generation, a second portion can be configured to perform tessellation and geometry shading, and a third portion can be configured to perform pixel shading or other screen-space operations to produce a rendered image for display. In at least one embodiment, intermediate data generated by one or more of the clusters 2114A-2114N can be stored in a buffer to allow intermediate data to be transferred between the clusters 2114A-2114N for further processing.

[0363] In at least one embodiment, the processing cluster array 2112 may receive processing tasks to be executed via a scheduler 2110, which receives commands defining the processing tasks from a front end 2108. In at least one embodiment, the processing task may include an index of data to be processed, such as surface (patch) data, raw data, vertex data, and / or pixel data, as well as state parameters and commands defining how the data is processed (e.g., what program to execute). In at least one embodiment, the scheduler 2110 may be configured to acquire an index corresponding to a task, or may receive an index from the front end 2108. In at least one embodiment, the front end 2108 may be configured to ensure that the processing cluster array 2112 is configured to be active before initiating the workload specified by an incoming command buffer (e.g., a batch buffer, push buffer, etc.).

[0364] In at least one embodiment, each of one or more instances of the parallel processing unit 2102 may be coupled to the parallel processor memory 2122. In at least one embodiment, the parallel processor memory 2122 may be accessed via a memory crossbar switch 2116, which may receive memory requests from the processing cluster array 2112 and the I / O unit 2104. In at least one embodiment, the memory crossbar switch 2116 may access the parallel processor memory 2122 via a memory interface 2118. In at least one embodiment, the memory interface 2118 may include a plurality of partition units (e.g., partition units 2120A, 2120B to 2120N), each of which may be coupled to a portion (e.g., a memory cell) of the parallel processor memory 2122. In at least one embodiment, the number of partition units 2120A-2120N is configured to be equal to the number of memory units, such that the first partition unit 2120A has a corresponding first memory unit 2124A, the second partition unit 2120B has a corresponding second memory unit 2124B, and the Nth partition unit 2120N has a corresponding Nth memory unit 2124N. In at least one embodiment, the number of partition units 2120A-2120N may not be equal to the number of memory units.

[0365] In at least one embodiment, memory cells 2124A-2124N may include various types of memory devices, including dynamic random access memory (DRAM) or graphics random access memory, such as synchronous graphics random access memory (SGRAM), including graphics double data rate (GDDR) memory. In at least one embodiment, memory cells 2124A-2124N may also include 3D stacked memory, including but not limited to high bandwidth memory (HBM). In at least one embodiment, rendering targets such as frame buffers or texture maps may be stored across memory cells 2124A-2124N, allowing partitioning cells 2120A-2120N to write portions of each rendering target in parallel to efficiently utilize the available bandwidth of the parallel processor memory 2122. In at least one embodiment, local instances of the parallel processor memory 2122 may be excluded to facilitate a unified memory design that utilizes system memory and local cache memory.

[0366] In at least one embodiment, any of clusters 2114A-2114N in the processing cluster array 2112 can process data to be written to any memory cell 2124A-2124N within the parallel processor memory 2122. In at least one embodiment, the memory crossbar switch 2116 can be configured to transfer the output of each cluster 2114A-2114N to any partition cell 2120A-2120N or another cluster 2114A-2114N, which can perform additional processing operations on the output. In at least one embodiment, each cluster 2114A-2114N can communicate with the memory interface 2118 via the memory crossbar switch 2116 to read from or write to various external memory devices. In at least one embodiment, the memory crossbar switch 2116 has a connection to a memory interface 2118 for communicating with I / O unit 2104, and a connection to a local instance of parallel processor memory 2122, enabling processing units within different processing clusters 2114A-2114N to communicate with system memory or other memory not local to parallel processing unit 2102. In at least one embodiment, the memory crossbar switch 2116 can use virtual channels to separate traffic flows between clusters 2114A-2114N and partition units 2120A-2120N.

[0367] In at least one embodiment, multiple instances of the parallel processing unit 2102 may be provided on a single add-in card, or multiple add-in cards may be interconnected. In at least one embodiment, different instances of the parallel processing unit 2102 may be configured to interoperate, even if the different instances have different numbers of processing cores, different numbers of local parallel processor memories, and / or other configuration differences. For example, in at least one embodiment, some instances of the parallel processing unit 2102 may include higher-precision floating-point units relative to other instances. In at least one embodiment, a system including one or more instances of the parallel processing unit 2102 or the parallel processor 2100 may be implemented in various configurations and form factors, including but not limited to desktop computers, laptop or handheld personal computers, servers, workstations, game consoles, and / or embedded systems.

[0368] Figure 21B This is a block diagram of a partitioning unit 2120 according to at least one embodiment. In at least one embodiment, the partitioning unit 2120 is... Figure 21AThis is an example of one of the partitioning units 2120A-2120N. In at least one embodiment, the partitioning unit 2120 includes an L2 cache 2121, a frame buffer interface 2125, and a ROP 2126 (raster operation unit). In at least one embodiment, the L2 cache 2121 is a read / write cache configured to perform load and store operations received from the memory crossbar switch 2116 and the ROP 2126. In at least one embodiment, the L2 cache 2121 outputs read misses and urgent write-back requests to the frame buffer interface 2125 for processing. In at least one embodiment, updates can also be sent to the frame buffer for processing via the frame buffer interface 2125. In at least one embodiment, the frame buffer interface 2125 communicates with memory cells in the parallel processor memory (such as...). Figure 21A It is coupled to one of the memory cells 2124A-2124N (e.g., within the parallel processor memory 2122).

[0369] In at least one embodiment, ROP 2126 is a processing unit that performs raster operations such as stenciling, z-testing, blending, etc. In at least one embodiment, ROP 2126 then outputs processed graphics data stored in graphics memory. In at least one embodiment, ROP 2126 includes compression logic for compressing depth or color data written to memory and decompressing depth or color data read from memory. In at least one embodiment, the compression logic may be lossless compression logic utilizing one or more of a variety of compression algorithms. In at least one embodiment, the type of compression performed by ROP 2126 may vary based on the statistical characteristics of the data to be compressed. For example, in at least one embodiment, incremental color compression is performed on depth and color data per tile.

[0370] In at least one embodiment, ROP 2126 is included within each processing cluster (e.g., Figure 21A The clusters 2114A-2114N are used instead of the partition unit 2120. In at least one embodiment, read and write requests for pixel data, rather than pixel fragment data, are transmitted via the memory crossbar switch 2116. In at least one embodiment, the processed graphics data can be displayed on a display device (such as...) Figure 20 Displayed on one or more display devices 2010, routed by processor 2002 for further processing, or by... Figure 21A One of the processing entities within the parallel processor 2100 is routed for further processing.

[0371] Figure 21C This is a block diagram of a processing cluster 2114 within a parallel processing unit according to at least one embodiment. In at least one embodiment, the processing cluster is... Figure 21AAn instance of one of the processing clusters 2114A-2114N. In at least one embodiment, the processing cluster 2114 can be configured to execute a number of threads in parallel, where a "thread" refers to an instance of a specific program executing on a particular set of input data. In at least one embodiment, a Single Instruction Multiple Data (SIMD) instruction issuing technique is used to support the parallel execution of a large number of threads without providing multiple independent instruction units. In at least one embodiment, a Single Instruction Multiple Threading (SIMT) technique is used to support the parallel execution of a large number of typically synchronized threads using a common instruction unit configured to issue instructions to a set of processing engines within each processing cluster.

[0372] In at least one embodiment, the operation of the processing cluster 2114 can be controlled via a pipeline manager 2132 that assigns processing tasks to the SIMT parallel processors. In at least one embodiment, the pipeline manager 2132... Figure 21A The scheduler 2110 receives instructions and manages the execution of these instructions via the graphics multiprocessor 2134 and / or texture unit 2136. In at least one embodiment, the graphics multiprocessor 2134 is an exemplary instance of a SIMT parallel processor. However, in at least one embodiment, the processing cluster 2114 may include various types of SIMT parallel processors with different architectures. In at least one embodiment, the processing cluster 2114 may include one or more instances of the graphics multiprocessor 2134. In at least one embodiment, the graphics multiprocessor 2134 can process data, and the data crossover switch 2140 can be used to allocate the processed data to one of a number of possible destinations, including other shader units. In at least one embodiment, the pipeline manager 2132 can facilitate the allocation of processed data by specifying the destination of the processed data to be allocated via the data crossover switch 2140.

[0373] In at least one embodiment, each graphics multiprocessor 2134 within the processing cluster 2114 may include the same set of functional execution logic (e.g., arithmetic logic units, load-memory units, etc.). In at least one embodiment, the functional execution logic may be configured in a pipelined manner, wherein new instructions may be issued before previous instructions complete. In at least one embodiment, the functional execution logic supports a variety of operations, including integer and floating-point arithmetic, comparison operations, Boolean operations, bit shifting, and computation of various algebraic functions. In at least one embodiment, the same functional unit hardware may be used to perform different operations, and any combination of functional units may exist.

[0374] In at least one embodiment, instructions sent to the processing cluster 2114 constitute threads. In at least one embodiment, a group of threads executing across a set of parallel processing engines is a thread group. In at least one embodiment, the thread group executes a general program on different input data. In at least one embodiment, each thread within the thread group can be assigned to a different processing engine within the graphics multiprocessor 2134. In at least one embodiment, the thread group may include fewer threads than the number of processing engines within the graphics multiprocessor 2134. In at least one embodiment, when the number of threads included in the thread group is less than the number of processing engines, one or more processing engines may be idle during a loop that is processing the thread group. In at least one embodiment, the thread group may also include more threads than the number of processing engines within the graphics multiprocessor 2134. In at least one embodiment, when the thread group includes more threads than the number of processing engines within the graphics multiprocessor 2134, processing can be performed in consecutive clock cycles. In at least one embodiment, multiple thread groups can be executed concurrently on the graphics multiprocessor 2134.

[0375] In at least one embodiment, the graphics multiprocessor 2134 includes an internal cache memory for performing load and store operations. In at least one embodiment, the graphics multiprocessor 2134 may forgo the internal cache and use a cache memory within the processing cluster 2114 (e.g., L1 cache 2148). In at least one embodiment, each graphics multiprocessor 2134 may also access partition units (e.g., Figure 21A The L2 cache is located within partition units 2120A-2120N, which are shared across all processing clusters 2114 and can be used to transfer data between threads. In at least one embodiment, the graphics multiprocessor 2134 can also access off-chip global memory, which may include one or more of local parallel processor memory and / or system memory. In at least one embodiment, any memory outside of the parallel processing unit 2102 can be used as global memory. In at least one embodiment, the processing cluster 2114 includes multiple instances of the graphics multiprocessor 2134, which can share common instructions and data that can be stored in the L1 cache 2148.

[0376] In at least one embodiment, each processing cluster 2114 may include a memory management unit (“MMU”) 2145 configured to map virtual addresses to physical addresses. In at least one embodiment, one or more instances of the MMU 2145 may reside in Figure 21AThe memory interface 2118 is located within the MMU 2145. In at least one embodiment, the MMU 2145 includes a set of page table entries (PTEs) for mapping virtual addresses to physical addresses of tiles and optionally to cache line indices. In at least one embodiment, the MMU 2145 may include an address translation lookup buffer (TLB) or a cache that may reside within the graphics multiprocessor 2134, the L1 cache 2148, or the processing cluster 2114. In at least one embodiment, physical addresses are processed to allocate surface data access locality for efficient request interleaving between partition units. In at least one embodiment, cache line indices may be used to determine whether a request for a cache line is a hit or a miss.

[0377] In at least one embodiment, the processing cluster 2114 can be configured such that each graphics multiprocessor 2134 is coupled to a texture unit 2136 to perform a texture mapping operation that determines texture sample locations, reads texture data, and filters texture data. In at least one embodiment, texture data is read from an internal texture L1 cache (not shown) or from an L1 cache within the graphics multiprocessor 2134, and texture data is retrieved from an L2 cache, local parallel processor memory, or system memory, as needed. In at least one embodiment, each graphics multiprocessor 2134 outputs a processed task to a data crossbar switch 2140 to provide the processed task to another processing cluster 2114 for further processing, or stores the processed task in an L2 cache, local parallel processor memory, or in system memory via a memory crossbar switch 2116. In at least one embodiment, a preROP 2142 (pre-raster operation unit) is configured to receive data from the graphics multiprocessor 2134 and direct the data to a ROP unit, which can be associated with a partitioning unit (e.g., [missing information]). Figure 21A The PreROP 2142 unit is located together with the partitioning units 2120A-2120N. In at least one embodiment, the PreROP 2142 unit can perform optimizations for color mixing, organizing pixel color data, and performing address translation.

[0378] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 may be used in the graphics processing cluster 2114 for performing inference or prediction operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases described herein.

[0379] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0380] Figure 21D A graphics multiprocessor 2134 according to at least one embodiment is illustrated. In at least one embodiment, the graphics multiprocessor 2134 is coupled to a pipeline manager 2132 of a processing cluster 2114. In at least one embodiment, the graphics multiprocessor 2134 has an execution pipeline including, but not limited to, an instruction cache 2152, an instruction unit 2154, an address mapping unit 2156, a register file 2158, one or more general-purpose graphics processing unit (GPGPU) cores 2162, and one or more load / store units 2166. In at least one embodiment, the GPGPU cores 2162 and the load / store units 2166 are coupled to a cache memory 2172 and a shared memory 2170 via a memory and cache interconnect 2168.

[0381] In at least one embodiment, instruction cache 2152 receives a stream of instructions to be executed from pipeline manager 2132. In at least one embodiment, instructions are cached in instruction cache 2152 and dispatched for execution by instruction unit 2154. In at least one embodiment, instruction unit 2154 may dispatch instructions as thread groups (e.g., thread bundles), wherein each thread in the thread group is assigned to a different execution unit within GPGPU core 2162. In at least one embodiment, instructions can access any local, shared, or global address space by specifying an address within a unified address space. In at least one embodiment, address mapping unit 2156 may be used to translate addresses in the unified address space into different memory addresses that can be accessed by load / store unit 2166.

[0382] In at least one embodiment, register file 2158 provides a set of registers for functional units of graphics multiprocessor 2134. In at least one embodiment, register file 2158 provides temporary storage for operands of data paths connected to functional units of graphics multiprocessor 2134 (e.g., GPGPU core 2162, load / store unit 2166). In at least one embodiment, register file 2158 is partitioned among each functional unit, such that a dedicated portion of register file 2158 is allocated to each functional unit. In at least one embodiment, register file 2158 is partitioned among different thread bundles being executed by graphics multiprocessor 2134.

[0383] In at least one embodiment, each GPGPU core 2162 may include a floating-point unit (FPU) and / or an integer arithmetic logic unit (ALU) for executing instructions of the graphics multiprocessor 2134. In at least one embodiment, the architectures of the various GPGPU cores 2162 may be similar or different. In at least one embodiment, a first portion of the GPGPU core 2162 includes a single-precision FPU and an integer ALU, while a second portion of the GPGPU core includes a double-precision FPU. In at least one embodiment, the FPU may implement the IEEE 754-2008 standard for floating-point algorithms or enable variable-precision floating-point algorithms. In at least one embodiment, the graphics multiprocessor 2134 may additionally include one or more fixed-function or special-function units for performing specific functions, such as copying rectangles or pixel blending operations. In at least one embodiment, one or more of the GPGPU cores 2162 may also include fixed-function or special-function logic.

[0384] In at least one embodiment, the GPGPU core 2162 includes SIMD logic capable of executing a single instruction on multiple sets of data. In at least one embodiment, the GPGPU core 2162 can physically execute SIMD4, SIMD8, and SIMD16 instructions, and logically execute SIMD1, SIMD2, and SIMD32 instructions. In at least one embodiment, the SIMD instructions for the GPGPU core can be generated by a shader compiler at compile time, or automatically generated when executing a program written and compiled for a Single Program Multiple Data (SPMD) or SIMT architecture. In at least one embodiment, multiple threads of a program configured for a SIMT execution model can be executed via a single SIMD instruction. For example, in at least one embodiment, eight SIMT threads performing the same or similar operations can be executed in parallel via a single SIMD8 logic unit.

[0385] In at least one embodiment, the memory and cache interconnect 2168 is an interconnect network connecting each functional unit of the graphics multiprocessor 2134 to the register file 2158 and the shared memory 2170. In at least one embodiment, the memory and cache interconnect 2168 is a cross-switch interconnect that allows the load / store unit 2166 to perform load and store operations between the shared memory 2170 and the register file 2158. In at least one embodiment, the register file 2158 can operate at the same frequency as the GPGPU core 2162, resulting in very low latency for data transfer between the GPGPU core 2162 and the register file 2158. In at least one embodiment, the shared memory 2170 can be used to implement communication between threads executing on functional units within the graphics multiprocessor 2134. In at least one embodiment, the cache memory 2172 can be used, for example, as a data cache for caching texture data communicated between functional units and texture units 2136. In at least one embodiment, the shared memory 2170 can also be used as a program-managed cache. In at least one embodiment, in addition to the automatically cached data stored in cache memory 2172, the thread executing on GPGPU core 2162 can also programmatically store data in shared memory.

[0386] In at least one embodiment, a parallel processor or GPGPU, as described herein, is communicatively coupled to a host / processor core to accelerate graphics operations, machine learning operations, pattern analysis operations, and various general-purpose GPU (GPGPU) functions. In at least one embodiment, the GPU may be communicatively coupled to the host processor / core via a bus or other interconnect (e.g., high-speed interconnects such as PCIe or NVLink). In at least one embodiment, the GPU may be integrated with the core on a package or chip and communicatively coupled to the core via an internal processor bus / interconnect within the package or chip. In at least one embodiment, regardless of how the GPU is connected, the processor core may assign work to the GPU in the form of a sequence of commands / instructions contained in a job descriptor. In at least one embodiment, the GPU then uses dedicated circuitry / logic to efficiently process these commands / instructions.

[0387] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8BDetails are provided regarding the inference and / or training logic 815. In at least one embodiment, the inference and / or training logic 815 may be used in the graphics multiprocessor 2134 for performing inference or prediction operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases described herein.

[0388] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0389] Figure 22 A multi-GPU computing system 2200 according to at least one embodiment is illustrated. In at least one embodiment, the multi-GPU computing system 2200 may include a processor 2202 coupled to a plurality of general-purpose graphics processing units (GPGPUs) 2206A-D via a host interface switch 2204. In at least one embodiment, the host interface switch 2204 is a PCI Express switch device that couples the processor 2202 to a PCI Express bus, through which the processor 2202 can communicate with the GPGPUs 2206A-D. In at least one embodiment, the GPGPUs 2206A-D may be interconnected via a set of high-speed P2P (peer-to-peer) GPU-to-GPU links 2216. In at least one embodiment, the GPU-to-GPU links 2216 are connected to each of the GPGPUs 2206A-D via dedicated GPU links. In at least one embodiment, the P2P GPU links 2216 enable direct communication between each GPGPU 2206A-D without communication via the host interface bus 2204 to which the processor 2202 is connected. In at least one embodiment, when GPU-to-GPU traffic is directed to the P2P GPU link 2216, the host interface bus 2204 remains available for system memory access or, for example, communication with other instances of the multi-GPU computing system 2200 via one or more network devices. While in at least one embodiment, the GPGPUs 2206A-D are connected to the processor 2202 via the host interface switch 2204, in at least one embodiment, the processor 2202 includes direct support for the P2P GPU link 2216 and can be directly connected to the GPGPUs 2206A-D.

[0390] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8BDetails are provided regarding inference and / or training logic 815. In at least one embodiment, inference and / or training logic 815 may be used in a multi-GPU computing system 2200 for performing inference or prediction operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases described herein.

[0391] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0392] Figure 23 This is a block diagram of a graphics processor 2300 according to at least one embodiment. In at least one embodiment, the graphics processor 2300 includes a ring interconnect 2302, a pipeline front end 2304, a media engine 2337, and graphics cores 2380A-2380N. In at least one embodiment, the ring interconnect 2302 couples the graphics processor 2300 to other processing units, said processing units including other graphics processors or one or more general-purpose processor cores. In at least one embodiment, the graphics processor 2300 is one of many processors integrated within a multi-core processing system.

[0393] In at least one embodiment, the graphics processor 2300 receives multiple batches of commands via a ring interconnect 2302. In at least one embodiment, the input commands are interpreted by a command streamer 2303 in a pipeline front-end 2304. In at least one embodiment, the graphics processor 2300 includes scalable execution logic for performing 3D geometry processing and media processing via graphics cores 2380A-2380N. In at least one embodiment, for 3D geometry processing commands, the command streamer 2303 provides the commands to the geometry pipeline 2336. In at least one embodiment, for at least some media processing commands, the command streamer 2303 provides the commands to a video front-end 2334, which is coupled to a media engine 2337. In at least one embodiment, the media engine 2337 includes a video quality engine (VQE) 2330 for video and image post-processing, and a multi-format encoding / decoding (MFX) engine 2333 for providing hardware-accelerated media data encoding and decoding. In at least one embodiment, the geometry pipeline 2336 and the media engine 2337 each generate an execution thread for thread execution resources provided by at least one graphics core 2380.

[0394] In at least one embodiment, the graphics processor 2300 includes scalable thread execution resources featuring graphics cores 2380A-2380N (which may be modular and sometimes referred to as core slices), each graphics core having multiple sub-cores 2350A-2350N, 2360A-2360N (sometimes referred to as core sub-slices). In at least one embodiment, the graphics processor 2300 may have any number of graphics cores 2380A. In at least one embodiment, the graphics processor 2300 includes graphics cores 2380A having at least a first sub-core 2350A and a second sub-core 2360A. In at least one embodiment, the graphics processor 2300 is a low-power processor with a single sub-core (e.g., 2350A). In at least one embodiment, the graphics processor 2300 includes multiple graphics cores 2380A-2380N, each graphics core including a set of first sub-cores 2350A-2350N and a set of second sub-cores 2360A-2360N. In at least one embodiment, each of the first sub-cores 2350A-2350N includes at least a first set of execution units 2352A-2352N and media / texture samplers 2354A-2354N. In at least one embodiment, each of the second sub-cores 2360A-2360N includes at least a second set of execution units 2362A-2362N and samplers 2364A-2364N. In at least one embodiment, each sub-core 2350A-2350N and 2360A-2360N shares a set of shared resources 2370A-2370N. In at least one embodiment, the shared resources include a shared cache memory and pixel operation logic.

[0395] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details are provided regarding inference and / or training logic 815. In at least one embodiment, inference and / or training logic 815 may be used in graphics processor 2300 for performing inference or prediction operations based at least in part on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases described herein.

[0396] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0397] Figure 24This is a block diagram illustrating a microarchitecture for a processor 2400 according to at least one embodiment, the processor 2400 including logic circuitry for executing instructions. In at least one embodiment, the processor 2400 can execute instructions, including x86 instructions, ARM instructions, special-purpose instructions for application-specific integrated circuits (ASICs), etc. In at least one embodiment, the processor 2400 may include registers for storing packaged data, such as 64-bit wide MMX data in a microprocessor implemented using Intel's MMX technology from Santa Clara, California. TM Registers. In at least one embodiment, an MMX register available in both integer and floating-point forms can operate with packed data elements accompanying Single Instruction Multiple Data (“SIMD”) and Streaming SIMD Extensions (“SSE”) instructions. In at least one embodiment, a 128-bit wide XMM register associated with SSE2, SSE3, SSE4, AVX, or later (beyond) (commonly referred to as “SSEx”) technology can hold such packed data operands. In at least one embodiment, processor 2400 can execute instructions to accelerate machine learning or deep learning algorithms, training, or inference.

[0398] In at least one embodiment, processor 2400 includes an ordered front end (“front end”) 2401 for fetching instructions to be executed and preparing instructions for later use in the processor pipeline. In at least one embodiment, front end 2401 may include several units. In at least one embodiment, instruction prefetcher 2426 fetches instructions from memory and feeds the instructions to instruction decoder 2428, which in turn decodes or interprets the instructions. For example, in at least one embodiment, instruction decoder 2428 decodes the received instructions into one or more machine-executable so-called “micro-operations” or “micro-instructions” (also referred to as “micro ops” or “uops”). In at least one embodiment, instruction decoder 2428 parses the instructions into opcodes and corresponding data and control fields, which can be used by the microarchitecture to perform operations according to at least one embodiment. In at least one embodiment, trace cache 2430 may assemble the decoded micro-operations into an ordered sequence or trace of programs in micro-operation queue 2434 for execution. In at least one embodiment, when the trace cache 2430 encounters a complex instruction, the microcode ROM 2432 provides the micro-operations required to complete the operation.

[0399] In at least one embodiment, some instructions may be converted into a single micro-operation, while others require several micro-operations to complete the entire operation. In at least one embodiment, if more than four micro-operations are required to complete an instruction, the instruction decoder 2428 may access the microcode ROM 2432 to execute the instruction. In at least one embodiment, an instruction may be decoded into a small number of micro-operations for processing at the instruction decoder 2428. In at least one embodiment, if multiple micro-operations are required to complete the operation, the instruction may be stored in the microcode ROM 2432. In at least one embodiment, the tracking cache 2430 references an entry point programmable logic array (“PLA”) to determine the correct micro-instruction pointer for reading a microcode sequence from the microcode ROM 2432 to complete one or more instructions, according to at least one embodiment. In at least one embodiment, after the microcode ROM 2432 has completed the serialization of the micro-operations of the instruction, the machine front end 2401 may resume fetching micro-operations from the tracking cache 2430.

[0400] In at least one embodiment, the out-of-order execution engine (“out-of-order engine”) 2403 can prepare instructions for execution. In at least one embodiment, the out-of-order execution logic has multiple buffers to smooth and reorder the instruction stream to optimize performance as the instruction stream moves down the pipeline and is scheduled for execution. In at least one embodiment, the out-of-order execution engine 2403 includes, but is not limited to, an allocator / register renamer 2440, a memory micro-operation queue 2442, an integer / floating-point micro-operation queue 2444, a memory scheduler 2446, a fast scheduler 2402, a slow / general-purpose floating-point scheduler (“slow / general-purpose FP scheduler”) 2404, and a simple floating-point scheduler (“simple FP scheduler”) 2406. In at least one embodiment, the fast scheduler 2402, the slow / general-purpose floating-point scheduler 2404, and the simple floating-point scheduler 2406 are also collectively referred to herein as “micro-operation schedulers 2402, 2404, 2406”. In at least one embodiment, the allocator / register renamer 2440 allocates the machine buffers and resources required for each micro-operation to execute. In at least one embodiment, the allocator / register renamer 2440 renames logical registers to entries in a register file. In at least one embodiment, the allocator / register renamer 2440 also allocates entries for each micro-operation in one of two micro-operation queues, preceding the memory scheduler 2446 and micro-operation schedulers 2402, 2404, 2406, with memory micro-operation queue 2442 for memory operations and integer / floating-point micro-operation queue 2444 for non-memory operations. In at least one embodiment, the micro-operation schedulers 2402, 2404, 2406 determine when a micro-operation is ready to be executed based on the readiness of their dependent input register operand sources and the availability of execution resources required for the micro-operation to complete its operation. In at least one embodiment, the fast scheduler 2402 may schedule on each half of the master clock cycle, while the slow / general-purpose floating-point scheduler 2404 and the simple floating-point scheduler 2406 may schedule once per master processor clock cycle. In at least one embodiment, micro-operation schedulers 2402, 2404, and 2406 arbitrate dispatch ports to schedule micro-operations for execution.

[0401] In at least one embodiment, execution block 2411 includes, but is not limited to, integer register file / bypass network 2408, floating-point register file / bypass network (“FP register file / bypass network”) 2410, address generation units (“AGU”) 2412 and 2414, fast arithmetic logic units (ALU) (“fast ALU”) 2416 and 2418, slow arithmetic logic unit (“slow ALU”) 2420, floating-point ALU (“FP”) 2422, and floating-point move unit (“FP move”) 2424. In at least one embodiment, integer register file / bypass network 2408 and floating-point register file / bypass network 2410 are also referred to herein as “register files 2408, 2410”. In at least one embodiment, AGUs 2412 and 2414, fast ALUs 2416 and 2418, slow ALU 2420, floating-point ALU 2422, and floating-point movement unit 2424 are also referred to herein as "execution units 2412, 2414, 2416, 2418, 2420, 2422, and 2424". In at least one embodiment, execution block 2411 may include, but is not limited to, any number (including zero) and type of register files, bypass networks, address generation units, and execution units in any combination.

[0402] In at least one embodiment, register networks 2408, 2410 may be arranged between micro-operation schedulers 2402, 2404, 2406 and execution units 2412, 2414, 2416, 2418, 2420, 2422, and 2424. In at least one embodiment, integer register file / bypass network 2408 performs integer operations. In at least one embodiment, floating-point register file / bypass network 2410 performs floating-point operations. In at least one embodiment, each of register networks 2408, 2410 may include, but is not limited to, a bypass network that can bypass a recently completed result that has not yet been written to a register file or forward it to a new relevant micro-operation. In at least one embodiment, register networks 2408, 2410 may communicate data with each other. In at least one embodiment, integer register file / bypass network 2408 may include, but is not limited to, two separate register files, one for low-order 32-bit data and the other for high-order 32-bit data. In at least one embodiment, the floating-point register file / bypass network 2410 may include, but is not limited to, 128-bit wide entries, since floating-point instructions typically have operands with widths ranging from 64 to 128 bits.

[0403] In at least one embodiment, execution units 2412, 2414, 2416, 2418, 2420, 2422, and 2424 can execute instructions. In at least one embodiment, register networks 2408 and 2410 store integer and floating-point data operation values ​​that the microinstructions need to execute. In at least one embodiment, processor 2400 may include, but is not limited to, any number of execution units 2412, 2414, 2416, 2418, 2420, 2422, and 2424, and combinations thereof. In at least one embodiment, floating-point ALU 2422 and floating-point move unit 2424 can perform floating-point, MMX, SIMD, AVX, and SSE or other operations, including specialized machine learning instructions. In at least one embodiment, floating-point ALU 2422 may include, but is not limited to, a 64-bit multiplication-64-bit floating-point divider for performing division, square root, and remainder micro-operations. In at least one embodiment, floating-point hardware can be used to process instructions involving floating-point values. In at least one embodiment, ALU operations can be passed to fast ALUs 2416 and 2418. In at least one embodiment, fast ALUs 2416 and 2418 can perform fast operations with an effective delay of half a clock cycle. In at least one embodiment, most complex integer operations are routed to slow ALU 2420, because slow ALU 2420 can include, but is not limited to, integer execution hardware for long-delay type operations, such as multipliers, shifters, flag logic, and branching. In at least one embodiment, memory load / store operations can be performed by ALUs 2412 and 2414. In at least one embodiment, fast ALU 2416, fast ALU 2418, and slow ALU 2420 can perform integer operations on 64-bit data operands. In at least one embodiment, fast ALU 2416, fast ALU 2418, and slow ALU 2420 can be implemented to support various data bit sizes, including sixteen, thirty-two, 128, 256, etc. In at least one embodiment, the floating-point ALU 2422 and the floating-point moving unit 2424 can be implemented to support a range of operands with various bit widths, such as supporting 128-bit wide packed data operands in conjunction with SIMD and multimedia instructions.

[0404] In at least one embodiment, micro-operation schedulers 2402, 2404, and 2406 dispatch dependent operations before the parent load has completed execution. In at least one embodiment, since micro-operations can be speculatively scheduled and executed within processor 2400, processor 2400 may also include logic for handling memory misses. In at least one embodiment, if a data load miss occurs in the data cache, there may be a dependent operation running in the pipeline that temporarily prevents the scheduler from accessing the correct data. In at least one embodiment, a replay mechanism tracks and re-executes instructions that use incorrect data. In at least one embodiment, replaying dependent operations may be necessary and may allow independent operations to complete. In at least one embodiment, the scheduler and replay mechanism of at least one embodiment of the processor may also be designed to capture instruction sequences used for text string comparison operations.

[0405] In at least one embodiment, "register" can refer to an onboard processor storage location that can be used as part of an instruction that identifies an operand. In at least one embodiment, a register can be one that can be used from outside the processor (from a programmer's perspective). In at least one embodiment, a register may not be limited to a particular type of circuitry. Rather, in at least one embodiment, a register can store data, provide data, and perform the functions described herein. In at least one embodiment, the registers described herein can be implemented by circuitry within the processor using any number of different techniques, such as dedicated physical registers, dynamically allocated physical registers renamed using register renaming, combinations of dedicated and dynamically allocated physical registers, etc. In at least one embodiment, an integer register stores 32-bit integer data. The register file of at least one embodiment also includes eight multimedia SIMD registers for packing data.

[0406] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details regarding inference and / or training logic 815 are provided. In at least one embodiment, some or all of the inference and / or training logic 815 may be incorporated into execution block 2411 and other memories or registers shown or not shown. For example, in at least one embodiment, the training and / or inference techniques described herein may use one or more ALUs shown in execution block 2411. Furthermore, weight parameters may be stored in on-chip or off-chip memory and / or registers (shown or not shown) that configure the ALUs of execution block 2411 to execute one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

[0407] Figure 25A deep learning application processor 2500 according to at least one embodiment is illustrated. In at least one embodiment, the deep learning application processor 2500 uses instructions that, if executed by the deep learning application processor 2500, cause the deep learning application processor 2500 to perform some or all of the processes and techniques described herein. In at least one embodiment, the deep learning application processor 2500 is an application-specific integrated circuit (ASIC). In at least one embodiment, as a result of executing one or more instructions or both, the application processor 2500 performs matrix multiplication operations or is "hardwired" into hardware. In at least one embodiment, the deep learning application processor 2500 includes, but is not limited to, processing clusters 2510(1)-2510(12), inter-chip links (“ICL”) 2520(1)-2520(12), inter-chip controllers (“ICC”) 2530(1)-2530(2), second-generation high-bandwidth memory (“HBM2”) 2540(1)-2540(4), memory controllers (“Mem Ctrlr”) 2542(1)-2542(4), high-bandwidth memory physical layers (“HBM PHY”) 2544(1)-2544(4), management controller central processing unit (“management controller CPU”) 2550, serial peripheral interfaces, internal integrated circuits, and general purpose input / output blocks (“SPI, I…”). 2 C, GPIO”)2560, Peripheral Component Interconnect Fast Controller and Direct Memory Access Block (“PCIe Controller and DMA”)2570, and Sixteen-Channel Peripheral Component Interconnect Fast Port (“PCI Express x 16”)2580.

[0408] In at least one embodiment, processing cluster 2510 can perform deep learning operations, including inference or prediction operations based on weight parameters computed using one or more training techniques (including those described herein). In at least one embodiment, each processing cluster 2510 can include, but is not limited to, any number and type of processors. In at least one embodiment, deep learning application processor 2500 can include any number and type of processing cluster 2500. In at least one embodiment, inter-chip link 2520 is bidirectional. In at least one embodiment, inter-chip link 2520 and inter-chip controller 2530 enable multiple deep learning application processors 2500 to exchange information, including activation information generated from executing one or more machine learning algorithms embodied in one or more neural networks. In at least one embodiment, deep learning application processor 2500 can include any number (including zero) and type of ICL 2520 and ICC 2530.

[0409] In at least one embodiment, the HBM2 2540 provides a total of 32GB of memory. In at least one embodiment, the HBM2 2540(i) is associated with both the memory controller 2542(i) and the HBM PHY 2544(i), where “i” is any integer. In at least one embodiment, any number of HBM2 2540s can provide any type and total amount of high-bandwidth memory and can be associated with any number (including zero) and type of memory controller 2542 and HBM PHY 2544. In at least one embodiment, any number and type of blocks implementing any number and type of communication standards can replace SPI, I... 2 C. GPIO 2560, PCIe controller and DMA 2570 and / or PCIe 2580.

[0410] Inference and / or training logic 815 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 8A and / or Figure 8B Details regarding the inference and / or training logic 815 are provided. In at least one embodiment, the deep learning application processor is used to train a machine learning model (such as a neural network) to predict or infer information provided to the deep learning application processor 2500. In at least one embodiment, the deep learning application processor 2500 is used to infer or predict information based on a trained machine learning model (e.g., a neural network) that has been trained by another processor or system or by the deep learning application processor 2500. In at least one embodiment, the processor 2500 may be used to perform one or more neural network use cases described herein.

[0411] In various embodiments, inference and / or training logic 815 is used to perform various feature matching, computer vision, or other operations described above. For example, inference and / or training logic 815 determines a feature set of a map and / or minimap in order to determine location information associated with a gaming session.

[0412] Figure 26This is a block diagram of a neuromorphic processor 2600 according to at least one embodiment. In at least one embodiment, the neuromorphic processor 2600 may receive one or more inputs from a source external to the neuromorphic processor 2600. In at least one embodiment, these inputs may be transmitted to one or more neurons 2602 within the neuromorphic processor 2600. In at least one embodiment, the neurons 2602 and their components may be implemented using circuitry or logic including one or more arithmetic logic units (ALUs). In at least one embodiment, the neuromorphic processor 2600 may include, but is not limited to, thousands or millions of instances of neurons 2602, but any suitable number of neurons 2602 may be used. In at least one embodiment, each instance of a neuron 2602 may include a neuron input 2604 and a neuron output 2606. In at least one embodiment, a neuron 2602 may generate an output that can be transmitted to the inputs of other instances of the neuron 2602. For example, in at least one embodiment, the neuron input 2604 and the neuron output 2606 may be interconnected via synapses 2608.

[0413] In at least one embodiment, neuron 2602 and synapse 2608 may be interconnected, causing neuromorphic processor 2600 to operate to process or analyze information received by neuromorphic processor 2600. In at least one embodiment, neuron 2602 may send an output pulse (or "fire" or "spike") when the input received through neuron input 2604 exceeds a threshold. In at least one embodiment, neuron 2602 may sum or integrate the signal received at neuron input 2604. For example, in at least one embodiment, neuron 2602 may be implemented as a leaky integrate-and-fire neuron, wherein if the summation (referred to as "membrane potential") exceeds a threshold, neuron 2602 may use a transfer function such as a sigmoid or threshold function to generate an output (or "fire"). In at least one embodiment, the leaky integrate-and-fire neuron may sum the signal received at neuron input 2604 to a membrane potential and may also apply an attenuation factor (or leakage) to reduce the membrane potential. In at least one embodiment, a leaky, integrated firing neuron may fire if multiple input signals are received at neuron input 2604 quickly enough to exceed a threshold (i.e., before the membrane potential decays too low to fire). In at least one embodiment, neuron 2602 may be implemented using circuitry or logic that receives input, integrates the input into a membrane potential, and decays the membrane potential. In at least one embodiment, the input may be averaged, or any other suitable transfer function may be used. Furthermore, in at least one embodiment, neuron 2602 may include, but is not limited to, comparator circuitry or logic that generates an output spike at neuron output 2606 when the result of applying the transfer function to neuron input 2604 exceeds a threshold. In at least one embodiment, once neuron 2602 fires, it can ignore previously received input information by, for example, resetting the membrane potential to 0 or another suitable default value. In at least one embodiment, once the membrane potential is reset to 0, neuron 2602 may resume normal operation after a suitable time period (or refractory period).

[0414] In at least one ...

Claims

1. A computer-implemented method, comprising: Use the first map data associated with the game session to obtain the first feature set; A second feature set is obtained using second map data acquired from a first image captured in the game session, the second map data corresponding to a subset of the first map data; Determine a third feature set that matches one or more features included in the first feature set and the second feature set; It is determined that the number of features in the third feature set is insufficient; As a result of insufficient determination of the number of features, local motion estimation is determined; and Using a transformation matrix calculated at least in part based on the third feature set, the location in the region corresponding to the first map data is determined, wherein determining the location in the region corresponding to the first map data further includes determining the location at least in part based on the local motion estimation.

2. The computer-implemented method according to claim 1, further comprising: The first map data associated with the gaming session is determined at least in part based on detecting icons representing the first map data captured from the gaming session.

3. The computer-implemented method according to claim 1, further comprising: The events of the game session are analyzed at least in part based on the location within the region.

4. The computer-implemented method according to claim 3, further comprising: The event is included in the activity feed associated with the gaming session.

5. The computer-implemented method according to claim 1, further comprising: The first feature set is stored using data storage and associated with the first map data.

6. The computer-implemented method according to claim 1, further comprising: The first map data was detected to be associated with the second game session; as well as Retrieve the first feature set from the data storage.

7. A computer-implemented method, comprising: Use the first map data associated with the game session to obtain the first feature set; A second feature set is obtained using second map data acquired from a first image captured in the game session, the second map data corresponding to a subset of the first map data; Determine a third feature set that matches one or more features included in the first feature set and the second feature set; The location in the region corresponding to the first map data is determined using a transformation matrix calculated at least in part based on the third feature set; It is determined that the number of features in the third feature set is insufficient; as well as As a result of determining that the number of said features is insufficient, the number of said features in the third feature set is increased at least in part based on a fourth feature set extracted from the third map data, which corresponds to another subset of the first map data.

8. A non-transitory computer-readable storage medium having stored executable instructions thereon, the executable instructions being executed by one or more processors of a computer system as a result of causing the computer system to: Use the first map data to extract the first feature set; A second feature set is extracted using second map data, which includes a subset of map data from the first map data. The number of features in the third feature set that match the features in the first feature set is insufficient; As a result of insufficient determination of the number of features, local motion estimation is determined; and Using a homography matrix determined at least in part based on matching features in a subset of the second feature set with features in the first feature set, the location in the region corresponding to the first map data is determined, wherein the location is determined at least in part based on the local motion estimation.

9. The non-transitory computer-readable storage medium of claim 8, wherein the subset of features is determined at least by: Determine a set of distances between features in the second feature set and features in the first feature set; and Features in the feature subset are selected at least in part based on values ​​included in the set of distances.

10. The non-transitory computer-readable storage medium of claim 9, wherein features in the feature subset are selected based at least in part on values ​​included in the set of distances that are higher than a threshold.

11. The non-transitory computer-readable storage medium of claim 9, wherein the distance in the set of distances includes the Euclidean distance between a first feature in the first feature set and a second feature in the second feature set.

12. The non-transitory computer-readable storage medium of claim 8, wherein the instructions further comprise instructions which, as a result of execution by the one or more processors of the computer system, cause the computer system to determine, at least in part, a map corresponding to at least one of the first map data or the second map data, based on a portion of an image obtained from a game session.

13. The non-transitory computer-readable storage medium of claim 8, wherein the instructions further include instructions that, as a result of execution by one or more processors of the computer system, cause the computer system to associate events during gameplay with the location.

14. The non-transitory computer-readable storage medium of claim 8, wherein the instructions further comprise instructions that, as a result of execution by the one or more processors of the computer system, cause the computer system to: It is determined that the number of features included in the feature subset is insufficient to determine the location; and Determine a local location estimate between a first location within a region corresponding to the second map data and a second location within a region corresponding to the third map data, wherein the third map data includes another subset of map data from the first map data but not the second map data.

15. The non-transitory computer-readable storage medium of claim 14, wherein the local location estimation is determined at least by: Identify the third set of features associated with the previous minimap; Determine a second subset of features that matches the features included in the third feature set with the features included in the second feature set; and The second homography matrix is ​​determined at least in part based on the second feature subset.

16. A system comprising: One or more processors are used for: The first feature vector set is generated using the feature set of the map associated with the game session; A second feature vector set is generated using the feature set of the minimap associated with the gaming session; The transformation matrix is ​​determined at least in part based on the matching of a first subset of eigenvectors in the first eigenvector set with a second subset of eigenvectors in the second eigenvector set; It is determined that the number of feature vectors in the first feature vector subset that match the second feature vector subset is insufficient; As a result of insufficient determination of the number of matching feature vectors, local motion estimation is determined; and The transformation matrix is ​​used to determine a location within the map, wherein the location is determined at least in part based on the local motion estimation.

17. The system of claim 16, wherein the minimap is captured from a portion of the image of the game session.

18. The system of claim 16, wherein the gaming session further includes video captured during application execution.

19. The system of claim 16, wherein the transformation matrix further comprises a scaling factor, a translation factor, and a rotation factor.

20. The system of claim 16, wherein the transformation matrix further comprises a homography matrix.

21. The system of claim 16, wherein the one or more processors are further configured to generate the first feature vector set based at least in part on the Scale Invariant Feature Transform (SIFT) algorithm.

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