297results about "Details involving image processing hardware" patented technology

Apparatus and method for efficient storage of BVH nodes in blocks. For example, one embodiment of an apparatus comprises: bounding volume hierarchy (BVH) construction circuitry to construct a BVH based on primitives of a graphics scene; and block allocation hardware logic coupled to or integral to the BVH construction circuitry, the block allocation hardware logic to allocate a plurality of nodes of the BVH into a plurality of blocks for storage in a cache or memory subsystem, the block allocation hardware logic to maximize a number of blocks which include a leading parent node and one or more corresponding child nodes of the plurality of nodes.

A method or system for implementing a machine learning pipeline on a chipset comprising a plurality of hardware compute elements. The system accesses a hardware-agnostic functional description of the machine learning pipeline, wherein the description specifies a plurality of functional modules, including at least one machine learning model. Hardware specifications of the chipset are accessed to identify the available hardware compute elements. Based on the hardware specifications, the functional modules are synthesized into a plurality of interconnected executable components configured to execute on at least two different hardware compute elements. An implementation package is generated, comprising the executable components and metadata describing interconnections between them. The implementation package is then deployed to the chipset, where the executable components are executed by the identified hardware compute elements.

In a general aspect, enhancement of artificial intelligence algorithms using 3D data is described. In some aspects, input data of an object is stored in a storage engine of a system. The input data includes first-order primitives and second-order primitives. A plurality of features of the object is determined by operation of an analytics engine of the system, based on the first-order primitives and the second-order primitives. A tensor field is generated by operation of the analytics engine of the system. The tensor field includes an attribute set, which includes one or more attributes selected from the first-order primitives, the second-order primitives, or the plurality of features. The tensor field is processed by operation of the analytics engine of the system according to a series of artificial intelligence algorithms to generate output data representing the object.

The invention discloses a multi-view collaborative 3D Gaussian splash optimization method and system, and the method comprises the steps: constructing a multi-level heterogeneous video memory pool, and dynamically dividing a video memory in 3D Gaussian reconstruction into a view exclusive memory block and a global shared memory pool; a mixed rendering-gradient pipeline is designed, and hardware-level pipeline parallelism in 3D Gaussian reconstruction is realized through a double-buffer asynchronous switching mechanism based on a CUDA Warp-level parallel primitive fusion forward rendering and back propagation thread group; performing multi-view gradient joint optimization, screening an effective gradient path in 3D Gaussian reconstruction based on the visibility mask matrix, and performing projection error weighted fusion on a multi-view gradient tensor; and implementing a multi-modal densification decision, generating a 3D Gaussian candidate splitting position in 3D Gaussian reconstruction through Monte Carlo sampling, calculating a joint optimization objective function by combining a multi-view projection residual error and a gradient contribution factor, and finally realizing 3D Gaussian reconstruction. According to the invention, high-precision and low-delay large-scale scene real-time rendering and training can be realized.

Approaches presented herein provide for the generation of synthesized data from input noise using a denoising diffusion network. A higher order differential equation solver can be used for the denoising process, with one or more higher-order terms being distilled into one or more separate efficient neural networks. A separate, efficient neural network can be called together with a primary denoising model at inference time without significant loss in sampling efficiency. The separate neural network can provide information about the curvature (or other higher-order term) of the differential equation, representing a denoising trajectory, that can be used by the primary diffusion network to denoise the image using fewer denoising iterations.

Approaches presented herein provide for the generation of synthesized data from input noise using a denoising diffusion network. A higher order differential equation solver can be used for the denoising process, with one or more higher-order terms being distilled into one or more separate efficient neural networks. A separate, efficient neural network can be called together with a primary denoising model at inference time without significant loss in sampling efficiency. The separate neural network can provide information about the curvature (or other higher-order term) of the differential equation, representing a denoising trajectory, that can be used by the primary diffusion network to denoise the image using fewer denoising iterations.

The invention relates to an image rendering method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of receiving rendering information; the rendering information is generated when the graphic application sends a rendering request; analyzing the rendering information, and creating a graphic task; the graphic task comprises a plurality of cache objects; the cache object is used for storing image data in the rendering process; the cache object carries a virtual address; when the physical space is allocated to each cache object, mapping the virtual address of each cache object to a physical address to obtain a page table mapping relation; generating a to-be-written page table item based on the page table mapping relation of each cache object, and sending the to-be-written page table item to the GPU; and the GPU writes the page table mapping relation into the corresponding page table item according to the to-be-written page table item so as to establish an access path between the GPU and the physical address, and the GPU executes the graphic task based on the access path. By adopting the method, the mapping efficiency can be improved.

Disclosed are an information processing method and a terminal device. The method comprises: acquiring first information, wherein the first information is information to be processed by a terminal device; calling an operation instruction in a calculation apparatus to calculate the first information so as to obtain second information; and outputting the second information. By means of the embodiments in the present disclosure, a calculation apparatus of a terminal device can be used to call an operation instruction to process first information, so as to output second information of a target desired by a user, thereby improving the information processing efficiency.

The invention provides a graphics processor, a texture loading method, a texture processing unit, equipment and a medium, and relates to the technical field of image texture loading. The method comprises the following steps: uniformly distributing texture loading instructions to a texture processing unit for execution, wherein the texture loading instructions comprise texture cache loading instructions; analyzing and executing the texture loading instruction through the texture processing unit; wherein the texture state information of the texture loading instruction is obtained through hardware analysis of the texture processing unit, the texture loading instruction completes address calculation and data reading in an independent texture loading pipeline, and the texture loading pipeline is started in the texture processing unit. According to the technical scheme, the scheduling logic of the texture loading instruction can be simplified, the compatibility of dynamic resources is improved, meanwhile, the hardware utilization rate is increased, and the reliability and execution efficiency of texture data access are improved.

The invention relates to the technical field of visual sensing modules, and discloses a sensing-storage-calculation integrated bionic visual sensing module in-storage calculation method and system.The method comprises the steps that photo-induced electrons excited by incident photons are directly injected into memristor oxygen vacancy conductive filaments through a shared electrode interface, and photocurrent driving signals are obtained; in a forward SET mode, driving memristor oxygen vacancy conductive filaments to form cross array conductivity distribution, and calculating row line driving voltage of a memristor cross array; applying the current to each cross point of cross array conductance distribution in a reverse RESET mode to obtain a flowing current of each cross point; and converging and summing the flowing current of each cross point at a column line node to obtain a column line output current, performing capacitor charging integration on the column line output current, and converting the column line output current into an analog domain convolution operation voltage, thereby avoiding intermediate multiple analog-to-digital and digital-to-analog conversion loss. And end-to-end low-delay and low-power-consumption processing from photon sensing to feature calculation is realized.

Executing a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: receiving, by a graphical processing unit (‘GPU’) server, a dataset transformed by a storage system that is external to the GPU server; and executing, by the GPU server, one or more machine learning algorithms using the transformed dataset as input.

The invention provides a polygon clipping processing method based on GPU acceleration, comprising the following steps: forming a minimum frame: reading the shape of a to-be-processed graph to form the minimum frame surrounding the to-be-processed graph, the minimum frame having a plurality of extreme points; the center of a first segmented grid is determined according to one of the extreme points, the segmented grid is a regular polygon, coordinates of all vertexes of the first segmented grid are obtained through calculation according to the center of the first segmented grid, other segmented grids are formed according to center coordinate deviation of the first segmented grid, and the center of the first segmented grid is a regular polygon; the minimum frame is completely covered by the segmentation grids; and kernel clipping: grouping the segmentation grids, performing parallel processing on each group of segmentation grids by using a GPU calculation unit so as to clip each group of segmentation grids edge by edge, and reserving vertexes of the segmentation grids in the to-be-processed graph and intersection points formed by the outer contour of the segmentation grids and the outer contour of the to-be-processed graph.

A method for performing Tile to Raster (T2R) conversion includes: receiving tile input data including a stream of a plurality of tiles each having a tile height, a tile input width, a macroblock width (MBW), and data bits; segmenting the tile input data based on a total number of virtual square tiles; segmenting a Tile Buffer (TB) into one or more of the virtual square tiles based on the received tile input data; and performing a raster-scanning operation on each of the segmented tile input data and the segmented TB based on the total number of virtual square tiles to generate raster data.

In a multi-GPU simulation environment, frame buffer management may be implemented by multiple GPUs (306, 402, 504, 600, 704) rendering respective frames of video, or by rendering respective portions of each frame of video (900 / 902; 1000 / 1002; 1100 / 1102). One of the GPUs controls HDMI frame output by virtue of receiving frame information from the other GPU(s) and reading out complete frames through a physically connected HDMI output port (1200). Or, the outputs of the GPUs can be multiplexed together (1302).

The invention provides a data migration method and device based on a computing pipeline, a processor and a related product. The data migration method based on the calculation pipeline comprises the following steps: receiving a data migration instruction of to-be-processed data; the data migration instruction is used for indicating a target data migration operation on the to-be-processed data; determining a target complexity classification corresponding to the target data migration operation based on a corresponding relationship between a preset data migration operation and the complexity classification; if the target complexity is classified as a preset complex operation type, converting the to-be-processed data and the data migration instruction into a general calculation task; the preset complex operation type comprises data conversion and data analysis in the data migration operation; and executing the general-purpose computing task through the general-purpose computing pipeline to obtain target data after the target data migration operation is completed. According to the method, the hardware and software complexity of the graphics processor for realizing data migration can be reduced.

Techniques are described for implementing selective activation and deactivation of a dynamically allocated subset of shader engines, such as based on application-based profile information and / or on an active system power configuration. Instructions for execution are received from an application associated with a first application profile.Based on the application profile, a quantity of activated shader engines in a plurality of shader engines is modified. The quantity of activated shader engines is further modified responsive to receiving additional instructions from a second application, and / or to receiving one or more indications of an altered active system power configuration.

Embodiments herein provide a method and system for a vicarious calibration of optical data from satellite sensors for urban scene flat fields. Identifying test sites automatically in the urban scene helps in vicarious calibration or on-board calibration of the hyperspectral / multispectral image. An internal average relative reflectance is calculated to get a relative reflectance of the image. Band ratios for various pixels is determined to assess flatness of the spectrum. Flat field candidates are identified from the various pixels having average band ratio nearing zero and a morphological technique is applied to determine a flat field. Finally, the image is calibrated vicariously based on the determined flat field as a test site. The on-board calibration of the remote sensing image may lead to a faster way to get the reflectance image of the scene, with the help of the calibration constants.

A processing device and method for tiled rendering of an image for display are provided. The processing device includes a memory and a processor. The processor is configured to receive an image including one or more three-dimensional (3D) objects, divide the image into tiles, perform coarse-level tiling on the tiles of the image, and perform fine-level tiling on the tiles of the image. The processing device also includes the same fixed-function hardware used to perform the coarse-level tiling and the fine-level tiling. The processor is configured to determine visibility information for a first tile. The visibility information is divided into draw-call visibility information and triangle visibility information for each remaining tile of the image.

To provide an imaging processing system that improves utilization efficiency of an FOV part in an imaging area.SOLUTION: A system of the present invention displays medical imaging data, and includes one or more data inputs, one or more processors, and one or more displays. The one or more data inputs are configured to receive first image data generated by a first medical imaging device. The first image data includes a field-of-vision (FOV) part and a non-FOV part (Step 202). The one or more processors identify the non-FOV part of the first image data (Step 204). By removing at least part of the non-FOV part of the first image data, cropped first image data is generated (Step 206). The cropped first image data to be displayed in a first part of the display and additional information to be displayed in a second part of the display are transmitted (Step 208).SELECTED DRAWING: Figure 2

The application relates to a data processing method and device, integrated circuit and electronic equipment, and belongs to the technical field of image processing. The data processing method comprises the following steps: alternately storing a received to-be-processed data stream into a first memory and a second memory; and continuously reading data from the first memory and the second memory for preprocessing, wherein the data writing rate is greater than the data reading rate, and is not greater than twice the data reading rate. The data processing method can process a high-speed data stream by using a low-speed processing module, thereby saving hardware cost and enhancing the applicability of the scheme.

The invention relates to a system and method for displaying medical imaging data. The system includes one or more data inputs configured to receive first image data generated by a first medical imaging device, where the first image data includes a field-of-view (FOV) portion and a non-FOV portion, and one or more displays configured to display the FOV portion and the non-FOV portion. And the one or more processors are configured to identify a non-FOV portion of the first image data and generate cropped first image data by removing at least a portion of the non-FOV portion of the first image data, and transmitting the cropped first image data for display in a first portion of the display and transmitting additional information for display in a second portion of the display.

A view of geometry captured in image data generated by an imaging sensor is compared with a description of the geometry in a volumetric data structure. The volumetric data structure describes the volume at a plurality of levels of detail and includes entries describing voxels defining subvolumes of the volume at multiple levels of detail. The volumetric data structure includes a first entry to describe voxels at a lowest one of the levels of detail and further includes a number of second entries to describe voxels at a higher, second level of detail, the voxels at the second level of detail representing subvolumes of the voxels at the first level of detail. Each of these entries include bits to indicate whether a corresponding one of the voxels is at least partially occupied with the geometry. One or more of these entries are used in the comparison with the image data.

The present application relates to the technical field of computers. Disclosed are a geometry stream data processing method and apparatus, a computing device, a storage medium, and a computer program product. The method is executed by a target geometry pipeline allocation module and includes: acquiring a data processing request, the data processing request indicating the topology of geometry stream data to be processed and whether the geometry stream data includes shearing point data; in response to the geometry stream not including the shearing point data, acquiring from the geometry stream target data to be split in the present batch; and, according to the topology, splitting the target data into data of corresponding primitive geometries. The geometry stream processing method of the present application is used for efficiently processing geometry stream, thereby solving the problem of how to improve the processing efficiency under the condition that a plurality of geometry pipelines are arranged to process the geometry stream.

An image inspection apparatus includes an image reader that reads an original image formed on a recording material based on a print job and generates a read image, and a hardware processor that analyzes the read image and performs an image inspection, wherein the hardware processor: acquires the read image from the image reader, detects an edge from the read image, and excludes a region near the edge from a target of the image inspection; performs a predetermined filter process on the read image after the exclusion process to generate a first reference image; compares the read image after the exclusion process with the first reference image to generate a first comparison image; and binarizes the first comparison image using a predetermined threshold to detect points where a specific abnormality has occurred, and outputs a detection result.

A system for storing geometry data for use by a graphics processor when rendering a frame that represents a view of a scene using a ray tracing process. The system comprises a processing circuit that is configured to prepare a ray tracing acceleration data structure for use when rendering a frame that represents a view of a scene using a ray tracing process. Graphics primitives for an end point of the ray tracing acceleration data structure is sorted into groups of contiguous graphics primitives, and fewer instances of vertex data is stored for a graphics primitive that is shared by at least two graphics primitives of the group of contiguous graphics primitives than the number of graphics primitives in the group of contiguous graphics primitives that share the vertex.

The present disclosure provides a management method and an apparatus for graphics rendering resources, a related device and a rendering method. The management method for graphics rendering resources includes: determining, in response to a received management instruction generated in a running process of at least one rendering program, a hardware resource object for calling graphic rendering, based on the management instruction and an interface function conversion relationship and a driving conversion relationship in a rendering resource object of a rendering program corresponding to the management instruction; and managing, according to the determined hardware resource object and the management instruction, a hardware resource of the graphic rendering required by the management instruction, wherein the interface function conversion relationship includes a conversion relationship between a pre-set hardware resource interface function and a target interface function of a hardware resource required for calling graphic rendering; and the driving conversion relationship includes a conversion relationship between the pre-set hardware resource interface function and the hardware resource object. The above method can reduce difficulty and cost of graphics rendering hardware resource management in scenarios with a plurality of rendering programs.

A method for configuring a buffer and an image synthesis apparatus are provided. The image synthesis apparatus includes a memory, and the memory includes a plurality of line buffers. The method includes the following step: dividing, according to a plurality of depths of a plurality of target planes, each of a plurality of target line buffers into a plurality of sections for respectively storing row pixel data of the target planes. The sections correspond to the depths of the target planes, respectively.

In various examples, apparatuses, systems, and techniques for performing offline image signal processing of source image data to generate target image data. In at least one embodiment, data collection is performed using exposure and calibration settings of an image sensor to generate source image data, which is then processed by using offline image signal processing to generate target data.

The invention relates to the technical field of vehicle-mounted display, in particular to an image display method and system, electronic equipment, a vehicle, a medium and a product. The image display method comprises the following steps: performing layer synthesis on first layer information to generate a first image, and performing layer synthesis on second layer information to generate a second image; and displaying the first image on a first display screen, and displaying the second image on a second display screen. According to the method, the first image is generated by performing layer synthesis on the first layer information, and meanwhile, the second image is generated by performing layer synthesis on the second layer information, so that the mode of performing layer synthesis on the display images of the multiple screens in parallel has higher layer synthesis efficiency; therefore, the image display fluency of each screen in the plurality of screens is higher.