800results about "Details involving image processing hardware" patented technology

Apparatuses, systems, and techniques to train neural networks. In at least one embodiment, a first neural network is trained to match accuracy of a second neural network independently of outputs of the second neural network based on, for example, a third neural network that generates weights for the first neural network.

Systems and methods for performing a medical imaging analysis task from PCCT (photon counting computed tomography) imaging data are provided. PCCT imaging data acquired from a PCCT imaging device is received. A plurality of PCCT virtual images is generated from the PCCT imaging data. A plurality of medical imaging analysis sub-tasks is performed based on the plurality of PCCT virtual images using a plurality of machine learning based networks. Results of the medical imaging analysis sub-tasks are combined to generate results of a medical imaging analysis task. The results of the medical imaging analysis task are output.

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.

Aspects presented herein relate to methods and devices for graphics processing including an apparatus, e.g., a GPU or CPU. The apparatus may obtain pixel information for a plurality of pixels in at least one frame included in a plurality of frames in a scene. The apparatus may also calculate a depth value for each of a first set of pixels. Further, the apparatus may identify whether each of the first set of pixels or a second set of pixels, or both, is occluded by at least one occluding object in the scene. The apparatus may configure a pattern mask configuration associated with a visibility mask for the plurality of pixels. The apparatus may also output, based on the pattern mask configuration, an indication of the depth value for the first set of pixels and coverage information for the first set of pixels or the second set of pixels.

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.

When generating a sequence of render outputs using a graphics processor, the completion status of rendering tasks for different render outputs is tracked so that processing tasks for later render outputs in the sequence of outputs can be processed concurrently with processing tasks for earlier render outputs in the sequence of outputs whilst ensuring that any dependencies between the rendering tasks are enforced.

The invention relates to the technical field of image processing, and discloses a real-time image processing method of a 3D and AI visual sensing visible light movement. The method comprises the following steps: carrying out bimodal channel image acquisition through an ISP image signal processing unit and a GPU acceleration unit to obtain a 3D image data stream and a visible light image data stream; performing illumination intensity detection and standardization processing to obtain standardized bimodal image data; inputting a dynamic convolutional neural network for feature fusion to obtain a multi-modal fusion feature map; performing space-time contrast learning on the multi-modal fusion feature map to obtain a first feature map; inputting the first feature graph into a graph structure redundancy evaluation model for feature selection to obtain a second feature graph; gPU parallel decoding and pipeline processing are carried out on the second feature map, a real-time processing image is output, the efficient real-time processing capacity is achieved, and the requirement for the real-time image processing response speed is met.

A computer implemented method for defect recognition in an imaging dataset of a wafer in a charged particle beam system comprising an embedded system, the method comprising: i) obtaining an imaging dataset of a wafer; ii) obtaining model data for a model architecture of a machine learning model for defect recognition in the imaging dataset of the wafer, the model architecture being implemented in the embedded system; iii) transferring the model data to a programmable memory of the embedded system; and iv) applying the machine learning model to an imaging dataset of a wafer to recognize defects, comprising executing the embedded system implemented model architecture with the transferred model data.

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.

Techniques are disclosed relating to distributing geometry work in a graphics processor. In some embodiments, multiple geometry pipelines are configured to process, in parallel, multiple segments of a set of geometry work. In some embodiments, control circuitry is configured to split operations from the set of graphics work into the multiple segments. This may include parsing a control stream for the set of graphics work to determine an initial work estimate based on numbers of primitives in different draw calls and adjusting the initial work estimate based on a complexity determination for the draw call.

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.

To display a low-resolution image at higher resolution and furthermore to reduce an afterimage.SOLUTION: Resolution is made higher by super-resolution processing. In this case, the super-resolution processing is performed after frame interpolation processing is performed. Further in that case, the super-resolution processing can be performed using a plurality of processing systems. Therefore, super-resolution processing can be performed at high speed even when frame frequency is made higher. An afterimage can be reduced since frame rate doubling is performed by frame interpolation processing.SELECTED DRAWING: Figure 1

An image capture system provides automated prompts for aiding a user in capturing images for use in 3D model creation. While a user is preparing to capture an image, the system provides visual indications that indicate whether a quality-based condition is satisfied. Based on the visual indications, a user can determine whether an image, if captured, would likely be suitable for use in creating a 3D model. Determining if the quality-based condition is satisfied may include monitoring output generated by one or more sensors and comparing the output against a threshold value. Additionally, the system may analyze the visual content or metadata associated with an image to determine if the quality-based condition is satisfied and request user input to further identify certain image features that were identified by the system.

A sliced graphics processing unit (GPU) architecture in a processor-based device is disclosed. In some aspects, a GPU based on a slicing GPU architecture includes a plurality of hardware slices. The GPU also includes command processor (CP) circuitry and a non-slice primitive controller (PCUS). Upon receiving a graphics instruction from a central processing unit (CPU), the CP circuitry determines a graphics workload and sends the graphics workload to the PCUS. The PCUS then divides the graphics workload into a plurality of sub-batches, and distributes each sub-batch to the PCS of the hardware slice for processing.

At least one processor may be configured to receive a first image frame of a sequence of image frames from an image capture device and select a first portion of a first image frame. The at least one processor may also be configured to obtain alignment information and determine a first portion and a second portion of a second image frame based on the alignment information. Further, the at least one processor may be configured to determine a bounding region within the second image frame and fetch image data corresponding to the bounding region of the second image frame from memory. In some examples, the first image frame may comprise a base image and the second image frame may comprise an alternative image frame. Further, the first image frame may comprise any one of the image frames of the sequence of image frames.

A system and a method for displaying super-resolution images generated from images of lower resolution, includes processor circuitry for a combination multi-core CPU and machine learning engine configured with an input for receiving the low resolution images, a feature extraction section to extract features from the low resolution images, non-linear feature mapping section, connected to the feature extraction section, generating feature maps using a self-calibrated block with pixel attention having a plurality of Depthwise Separable Convolution (DSC) layers, a late upsampling section combines at least one DSC layer and a skip connection that upsamples the feature maps to a predetermined dimension, and a video output for displaying approximate upsampled super-resolution images that corresponds to the low resolution images.

The invention provides a frame insertion processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a first texture and a second texture obtained through rendering before the first texture, the first texture and the second texture are UI-free textures, splicing the first texture and the second texture to obtain a spliced texture, and storing the spliced texture in a storage medium; a feature extraction network in a frame insertion model is adopted to perform multi-dimensional feature extraction on the spliced texture to obtain a target optical flow feature map and a target mask feature map, the feature extraction network is deployed in an NPU, and a frame insertion network in the frame insertion model is adopted to obtain frame insertion texture according to the target optical flow feature map and the target mask feature map, the frame insertion network is deployed in the GPU, and the frame insertion model is deployed in the GPU and the NPU, so that the hybrid programming of the GPU and the NPU is realized, the algorithm advantages of two kinds of hardware are fully played, and the processing efficiency of the frame insertion model is improved.

An image capture system provides automated prompts for aiding a user in capturing images for use in 3D model creation. While a user is preparing to capture an image, the system provides visual indications that indicate whether a quality-based condition is satisfied. Based on the visual indications, a user can determine whether an image, if captured, would likely be suitable for use in creating a 3D model. Determining if the quality-based condition is satisfied may include monitoring output generated by one or more sensors and comparing the output against a threshold value. Additionally, the system may analyze the visual content or metadata associated with an image to determine if the quality-based condition is satisfied and request user input to further identify certain image features that were identified by the system.

A processor includes a first processing pipeline, a second processing pipeline and a memory management that allocates memory regions from memory for the first processing pipeline to write the data of each of a first of a sequence of tasks, and deallocates each of the memory regions after the data therein has been processed by the second processing pipeline. A blocking circuit enables the second processing pipeline to start processing a second sequence of tasks while the memory management circuit is still deallocating some of the memory regions allocated to the data portions of the first of said sequence of tasks, the blocking circuit preventing identifiers of the data portions of the second task being passed to the memory management circuit until the memory management circuit indicates that it has completed deallocating the memory regions allocated to all the data portions of the first task.

Aspects presented herein relate to methods and apparatus for graphics processing, including devices, such as GPUs. The apparatus may receive a drawing call instruction set corresponding to the graphics workload, wherein the drawing call instruction set is associated with at least one runtime parameter. The apparatus may also obtain a first shader program associated with storing the data in the system memory and at least one second shader program associated with storing the data in the constant memory. Further, the apparatus may execute the first shader program or the at least one second shader program based on whether the at least one runtime parameter is less than or equal to a size of the constant memory. The apparatus may also update or maintain a configuration of a shader processor or a stream processor based on executing the first shader program or the at least one second shader program.

The embodiment of the invention provides a screen cleaning method and device, electronic equipment and a readable medium, and relates to the technical field of display, in the method, vertex coordinates, width pixel values and height pixel values defined in a predefined screen cleaning area structural body and screen cleaning color values defined in a screen cleaning color structural body are pushed to a designated register of a GPU. And respectively reading the vertex coordinate, the width pixel value, the height pixel value and the clear screen color value from the specified register by a shader. And rendering all pixels in the display area indicated by the vertex coordinates, the width pixel value and the height pixel value according to the screen cleaning color value to realize screen cleaning. The vertex coordinate, the width pixel value, the height pixel value and the screen cleaning color value are directly pushed to the specified register, so that a shader can directly and conveniently obtain the vertex coordinate, the width pixel value, the height pixel value and the screen cleaning color value required by screen cleaning from the specified register. The screen cleaning efficiency can be improved, and efficient screen cleaning is achieved.

The invention provides a rendering system, a chip, equipment and a rendering method applied to the rendering system, and relates to the technical field of image rendering. The rendering system comprises a rendering pipeline manager, a middle buffer area, M geometric processing pipelines and N pixel processing pipelines, wherein a plurality of data segments divided from an input data stream of the rendering system are allocated to M geometric processing pipelines for processing, and primitive data obtained by processing each data segment is stored in a middle buffer area; the rendering pipeline manager is used for acquiring serial numbers and memory information of the data segments processed by the geometric processing pipeline; the rendering pipeline manager is further used for sending rendering instructions to the N pixel processing pipelines respectively, the rendering instructions comprise the serial numbers and the memory information of the k data segments, k is an integer larger than or equal to 1, and the serial numbers of the k data segments are continuous under the condition that k is larger than 1. According to the method, the image rendering efficiency is improved.

Methods, systems, and computer program products simulate image-capture of a scene at having least one object of interest (OOI) by a camera. A plurality of rendered images is obtained and includes: i) a first image, at a first resolution, that is an image of the scene and excludes the OOI, ii) a second image, at a second resolution higher than the first resolution, that is an image of the OOI, iii) a third image, at the second resolution, that is an image of a region that bounds the OOI, wherein the OOI at least partially affects one or more other objects in the region, and iv) a fourth image, at the second resolution, wherein for each pixel of the fourth image, a pixel value of the pixel indicates a contribution of the OOI in the pixel. The images are processed to produce a high-resolution image of the OOI.

An Artificial Intelligence (AI) computational system for generating an embryo viability score from a single image of an embryo to aid selection of an embryo for implantation in an In-Vitro Fertilisation (IVF) procedure is described. The AI model uses a deep learning method applied to images in which the Zona Pellucida region in the image is identified using segmentation, and ground truth labels such as detection of a heartbeat at a six week ultrasound scan.

The invention discloses a video memory management method and system, a chip and electronic equipment, and belongs to the technical field of storage. The method comprises the following steps: acquiring respective heat information of a plurality of storage units included in a first chip through a first logic unit, wherein the heat information is used for indicating use conditions of the storage units; data in at least one target storage unit is compressed through a second logic unit, and the target storage unit is determined from the multiple storage units by the second chip based on the obtained heat information of the multiple storage units; wherein the first chip is different from the second chip. According to the method, the target storage unit can be determined from the multiple storage units based on the use condition of the storage units, and the data in the target storage unit is compressed, so that the storage space occupied by the data in the video memory is reduced, and the available storage capacity of the video memory is effectively expanded.

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.

For direct multi-volume rendering, each voxel of a volume has a scalar value. A multi-ray generator generates a view multi-ray in the direction of a pixel of a projection image, wherein the pixel represents a 2D projection of all scene voxels intersecting with the view multi-ray behind the projection image. A volume ray marching processor processes each view ray, wherein, at each ray marching step, the scalar value of the corresponding voxel is mapped to a color value and a transparency value of said corresponding voxel. A projection image updater updates the value of the particular pixel in the projection image by combining the respective voxel color and transparency values of the individual view rays. Updating with the values of a particular voxel intersecting with a particular view ray is performed after the updating with all intersecting voxel values of voxels closer to the viewing point than the particular voxel.

The invention provides a chip system and a method for reading data, relates to the technical field of chips, and improves the performance of a graphics processor. According to the specific scheme, the chip system comprises a graphics processor and a memory manager, and the graphics processor is used for responding to a graphics rendering task and outputting a first reading instruction to the memory manager. And the memory manager is used for reading the first data from the memory based on the first virtual address and the partition mapping table in response to the first reading instruction. The memory manager is further used for outputting the first data to the graphics processor. The method is used for the process that the graphics processor reads the data in the memory.