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

Abstract: A processing device is provided which includes memory and a processor. The processor is configured to receive an input image having a first resolution, generate at least one linear down-sampled version of the input image via a linear upscaling network, generate at least one non-linear down-sampled version of the input image via a non-linear upscaling network, extract a first feature map from the at least one linear down-sampled version of the input image, and extract a second feature map from the at least one non-linear down-sampled version of the input image. The processor is also configured to convert the at least one linear down-sampled version of the input image and the at least one non-linear down-sampled version of the input image into pixels of an output image having a second resolution higher than the first resolution using the first feature map and the second feature map.

Abstract: Aspects of this disclosure relate to a process for rendering graphics that includes performing, with a hardware unit of a graphics processing unit (GPU) designated for vertex shading, a vertex shading operation to shade input vertices so as to output vertex shaded vertices, wherein the hardware unit adheres to an interface that receives a single vertex as an input and generates a single vertex as an output. The process also includes performing, with the hardware unit of the GPU designated for vertex shading, a hull shading operation to generate one or more control points based on one or more of the vertex shaded vertices, wherein the one or more hull shading operations operate on at least one of the one or more vertex shaded vertices to output the one or more control points.

Abstract: Aspects of this disclosure relate to a process for rendering graphics that includes performing, with a hardware unit of a graphics processing unit (GPU) designated for vertex shading, a vertex shading operation to shade input vertices so as to output vertex shaded vertices, wherein the hardware unit adheres to an interface that receives a single vertex as an input and generates a single vertex as an output. The process also includes performing, with the hardware unit of the GPU designated for vertex shading, a hull shading operation to generate one or more control points based on one or more of the vertex shaded vertices, wherein the one or more hull shading operations operate on at least one of the one or more vertex shaded vertices to output the one or more control points.

Abstract: Techniques for performing shader operations are provided. The techniques include, performing pixel shading at a shading rate defined by pixel shader variable rate shading (“VRS”) data, and updating the pixel VRS data that indicates one or more shading rates for one or more tiles based on whether the tiles of the one or more tiles include triangle edges or do not include triangle edges, to generate updated VRS data.

Abstract: A processing device is provided which includes memory configured to store data and a processor configured to determine, based on convolutional parameters associated with an image, a virtual general matrix-matrix multiplication (GEMM) space of a virtual GEMM space output matrix and generate, in the virtual GEMM space output matrix, a convolution result by matrix multiplying the data corresponding to a virtual GEMM space input matrix with the data corresponding to a virtual GEMM space filter matrix. The processing device also includes convolutional mapping hardware configured to map, based on the convolutional parameters, positions of the virtual GEMM space input matrix to positions of an image space of the image.

Abstract: Techniques for performing shader operations are provided. The techniques include, performing pixel shading at a shading rate defined by pixel shader variable rate shading (“VRS”) data, and updating the pixel VRS data that indicates one or more shading rates for one or more tiles based on whether the tiles of the one or more tiles include triangle edges or do not include triangle edges, to generate updated VRS data.

Abstract: The present disclosure provides for automatic peering between virtual networks, such as virtual private clouds (VPCs). A VPC may be configured to operate in an “auto-peering” mode, allowing for automatic peering to be turned on or off. When auto-peering is turned on, that VPC may seek connections with other VPCs, such as other VPCs matching one or more predefined policies. In addition, the particular VPC with auto-peering turned on may be open to accept connection requests from other VPCs matching one or more predefined policies. The policies for requesting connection may be the same as or different than the policies for accepting connection requests. According to some examples, the VPC may be set to a “listening” mode, in which it is open to peering with any other VPC that matches a predefined policy, but is not actively seeking to establish other connections.

Abstract: A processing device is provided which includes memory and a processor. The processor is configured to receive an input image having a first resolution, generate at least one linear down-sampled version of the input image via a linear upscaling network, generate at least one non-linear down-sampled version of the input image via a non-linear upscaling network, extract a first feature map from the at least one linear down-sampled version of the input image, and extract a second feature map from the at least one non-linear down-sampled version of the input image. The processor is also configured to convert the at least one linear down-sampled version of the input image and the at least one non-linear down-sampled version of the input image into pixels of an output image having a second resolution higher than the first resolution using the first feature map and the second feature map.

Abstract: A processing device is provided which includes memory and a processor. The processor is configured to receive an input image having a first resolution, generate linear down-sampled versions of the input image by down-sampling the input image via a linear upscaling network and generate non-linear down-sampled versions of the input image by down-sampling the input image via a non-linear upscaling network. The processor is also configured to convert the down-sampled versions of the input image into pixels of an output image having a second resolution higher than the first resolution and provide the output image for display.

Abstract: A graphics processing unit (GPU) for clustering of machine learning (ML) functional components, including: a plurality of compute units; a plurality of ML clusters, wherein each of the ML clusters comprises at least one arithmetic logic unit (ALU), and wherein each of the ML clusters is associated with a respective subset of the compute units; and a plurality of memory modules each positioned on the GPU adjacent to a respective ML cluster of the plurality of ML clusters, wherein each ML cluster is configured to directly access one or more adjacent memory modules.

Abstract: A technique for performing ray tracing operations is provided. The technique includes performing bounding volume hierarchy (“BVH”) traversal in multiple accelerated processing devices (“APDs”), utilizing bounding volume hierarchy data copies in memories local to the multiple APDs; rendering primitives determined to be intersected based on the BVH traversal, using geometry information and texture data spread across the memories local to the multiple APDs; and storing results of rendered primitives for a set of tiles assigned to the multiple APDs into tile buffers stored in APD memories local to the APDs.

Abstract: The SYNTHETIC CONTROL GENERATION AND CAMPAIGN IMPACT ASSESSMENT APPARATUSES, METHODS AND SYSTEMS (“SCG”) provides a platform that, in various embodiments, is configurable to evaluate efficacy and/or return on investment of advertising and/or other media campaigns and/or to recommend actions for improvement thereof. In some implementations, multi-faceted campaigns of media and/or advertising behavior (e.g., including one or more of: internet advertising, television advertising, radio advertising, print advertising, social media publication, product placement, and/or the like) may be considered as a whole in relation to global metric behaviors and/or patterns in order to evaluate the efficacy and/or return on investment associated with the campaign as a whole.

Abstract: Aspects of this disclosure relate to a process for rendering graphics that includes performing, with a hardware unit of a graphics processing unit (GPU) designated for vertex shading, a vertex shading operation to shade input vertices so as to output vertex shaded vertices, wherein the hardware unit adheres to an interface that receives a single vertex as an input and generates a single vertex as an output. The process also includes performing, with the hardware unit of the GPU designated for vertex shading, a hull shading operation to generate one or more control points based on one or more of the vertex shaded vertices, wherein the one or more hull shading operations operate on at least one of the one or more vertex shaded vertices to output the one or more control points.

Abstract: Techniques for performing shader operations are provided. The techniques include, performing pixel shading at a shading rate defined by pixel shader variable rate shading (“VRS”) data, and updating the pixel VRS data that indicates one or more shading rates for one or more tiles based on whether the tiles of the one or more tiles include triangle edges or do not include triangle edges, to generate updated VRS data.

Abstract: A texture processor based ray tracing accelerator method and system are described. The system includes a shader, texture processor (TP) and cache, which are interconnected. The TP includes a texture address unit (TA), a texture cache processor (TCP), a filter pipeline unit and a ray intersection engine. The shader sends a texture instruction which contains ray data and a pointer to a bounded volume hierarchy (BVH) node to the TA. The TCP uses an address provided by the TA to fetch BVH node data from the cache. The ray intersection engine performs ray-BVH node type intersection testing using the ray data and the BVH node data. The intersection testing results and indications for BVH traversal are returned to the shader via a texture data return path. The shader reviews the intersection results and the indications to decide how to traverse to the next BVH node.

Abstract: Aspects of this disclosure relate to a process for rendering graphics that includes performing, with a hardware unit of a graphics processing unit (GPU) designated for vertex shading, a vertex shading operation to shade input vertices so as to output vertex shaded vertices, wherein the hardware unit adheres to an interface that receives a single vertex as an input and generates a single vertex as an output. The process also includes performing, with the hardware unit of the GPU designated for vertex shading, a hull shading operation to generate one or more control points based on one or more of the vertex shaded vertices, wherein the one or more hull shading operations operate on at least one of the one or more vertex shaded vertices to output the one or more control points.

Abstract: Described herein are techniques for improving the effectiveness of depth culling. In a first technique, a binner is used to sort primitives into depth bins. Each depth bin covers a range of depths. The binner transmits the depth bins to the screen space pipeline for processing in near-to-far order. Processing the near bins first results in the depth buffer being updated, allowing fragments for the primitives in the farther bins to be culled more aggressively than if the depth binning did not occur. In a second technique, a buffer is used to initiate two-pass processing through the screen space pipeline. In the first pass, primitives are sent down to update the depth block and are then culled. The fragments are processed normally in the second pass, with the benefit of the updated depth values.

Abstract: A device is disclosed. The device includes a machine learning die including a memory and one or more machine learning accelerators; and a processing core die stacked with the machine learning die, the processing core die being configured to execute shader programs for controlling operations on the machine learning die, wherein the memory is configurable as either or both of a cache and a directly accessible memory.

Abstract: Techniques for performing shader operations are provided. The techniques include, performing pixel shading at a shading rate defined by pixel shader variable rate shading (“VRS”) data, updating the pixel VRS data that indicates one or more shading rates for one or more tiles based on whether the tiles of the one or more tiles include triangle edges or do not include triangle edges, to generate updated VRS data, and writing a VRS rate feedback buffer based on the updated VRS data.