Abstract: One embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (SIMT) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute an intermediate product of 16-bit operands and to compute a 32-bit sum based on the intermediate product.

Abstract: Embodiments described herein provide a technique to facilitate the broadcast or multicast of asynchronous loads to shared local memory of a plurality of graphics cores within a graphics core cluster. One embodiment provides a graphics processor including a cache memory a graphics core cluster coupled with the cache memory. The graphics core cluster includes a plurality of graphics cores. The plurality of graphics cores includes a graphics core configured to receive a designation as a producer graphics core for a multicast load, read data from the cache memory; and transmit the data read from the cache memory to a consumer graphics core of the plurality of graphics cores.

Abstract: One embodiment provides circuitry coupled with cache memory and a memory interface, the circuitry to compress compute data at multiple cache line granularity, and a processing resource coupled with the memory interface and the cache memory. The processing resource is configured to perform a general-purpose compute operation on compute data associated with multiple cache lines of the cache memory. The circuitry is configured to compress the compute data before a write of the compute data via the memory interface to the memory bus, in association with a read of the compute data associated with the multiple cache lines via the memory interface, decompress the compute data, and provide the decompressed compute data to the processing resource.

Abstract: One embodiment provides for a graphics processing unit comprising a processing cluster to perform multi-rate shading via coarse pixel shading and output shaded coarse pixels for processing by a post-shader pixel processing pipeline.

Abstract: One embodiment provides a graphics processor comprising memory access circuitry configured to generate a virtual address for pixel data at a pixel coordinate on a surface in memory to facilitate the caching of the pixel data in a cache memory before the actual memory address of the pixel coordinate is able to be determined.

Abstract: A library of machine learning primitives is provided to optimize a machine learning model to improve the efficiency of inference operations. In one embodiment a trained convolutional neural network (CNN) model is processed into a trained CNN model via pruning, convolution window optimization, and quantization.

Abstract: An embodiment of an electronic processing system may include an application processor, persistent storage media communicatively coupled to the application processor, a graphics subsystem communicatively coupled to the application processor, an object space adjuster communicatively coupled to the graphics subsystem to adjust an object space parameter based on a screen space parameter, and a sample adjuster communicatively coupled to the graphics subsystem to adjust a sample parameter of the graphics subsystem based on a detected condition. Other embodiments are disclosed and claimed.

Abstract: Embodiments are generally directed to a multi-tile architecture for graphics operations. An embodiment of an apparatus includes a multi-tile architecture for graphics operations including a multi-tile graphics processor, the multi-tile processor includes one or more dies; multiple processor tiles installed on the one or more dies; and a structure to interconnect the processor tiles on the one or more dies, wherein the structure to enable communications between processor tiles the processor tiles.

Abstract: In one embodiment, a processor includes: a graphics processor to execute a workload; and a power controller coupled to the graphics processor. The power controller may include a voltage ramp circuit to receive a request for the graphics processor to operate at a first performance state having a first operating voltage and a first operating frequency and cause an output voltage of a voltage regulator to increase to the first operating voltage. The voltage ramp circuit may be configured to enable the graphics processor to execute the workload at an interim performance state having an interim operating voltage and an interim operating frequency when the output voltage reaches a minimum operating voltage. Other embodiments are described and claimed.

Abstract: Systems, apparatuses and methods may provide a way to monitor, by a process monitor, one or more processing factors of one or more client devices hosting one or more user sessions. More particularly, the systems, apparatuses and methods may provide a way to generate, responsively, a scene generation plan based on one or more of a digital representation of an N dimensional space or at least one of the one or more processing factors, and generate, by a global scene generator, a global scene common to the one or more client devices based on the digital representation of the space. The systems, apparatuses and methods may further provide for performing, by a local scene generator, at least a portion of the global illumination based on one or more of the scene generation plan, or application parameters.

Abstract: One embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (SIMT) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute an intermediate product of 16-bit operands and to compute a 32-bit sum based on the intermediate product.

Abstract: Apparatus and method for efficient graphics processing including ray tracing. For example, one embodiment of a graphics processor comprises: execution hardware logic to execute graphics commands and render images; an interface to couple functional units of the execution hardware logic to a tiled resource; and a tiled resource manager to manage access by the functional units to the tiled resource, a functional unit of the execution hardware logic to generate a request with a hash identifier (ID) to request access to a portion of the tiled resource, wherein the tiled resource manager is to determine whether a portion of the tiled resource identified by the hash ID exists, and if not, to allocate a new portion of the tiled resource and associate the new portion with the hash ID.

Abstract: One embodiment provides an apparatus comprising a memory device configured to store a page table that includes a set of page table entries and a graphics processing cluster array including a plurality of graphics multiprocessors, the plurality of graphics multiprocessors coupled via a data interconnect. The graphics multiprocessor of the plurality of graphics multiprocessors includes a translation lookaside buffer (TLB) coupled with the memory device, the TLB to cache a first page table entry of the set of page table entries, the first page table entry to indicate that a first virtual page is a valid page that is cleared to a clear color and circuitry to bypass an access to the memory device for the first virtual page and determine a color associated with the first virtual page based on the indication that the first virtual page is a valid page that is cleared to the clear color.

Abstract: A method of embodiments, as described herein, includes detecting thread groups relating to machine learning associated with one or more processing devices. The method may further include facilitating barrier synchronization of the thread groups across multiple dies such that each thread in a thread group is scheduled across a set of compute elements associated with the multiple dies, where each die represents a processing device of the one or more processing devices, the processing device including a graphics processor.

Abstract: An apparatus and method are described for managing data which is biased towards a processor or a GPU. For example, an apparatus comprises a processor comprising one or more cores, one or more cache levels, and cache coherence controllers to maintain coherent data in the one or more cache levels; a graphics processing unit (GPU) to execute graphics instructions and process graphics data, wherein the GPU and processor cores are to share a virtual address space for accessing a system memory; a GPU memory addressable through the virtual address space shared by the processor cores and GPU; and bias management circuitry to store an indication for whether the data has a processor bias or a GPU bias, wherein if the data has a GPU bias, the data is to be accessed by the GPU without necessarily accessing the processor's cache coherence controllers.

Abstract: Thread dispatch circuitry is configured to dispatch threads of a two-dimensional (2D) thread group based on data access locality associated with the threads. The thread dispatch circuitry can dispatch a first 2D sub-group of the 2D thread group to a compute block of the multiple compute blocks, the first 2D sub-group associated with a first 2D tile of memory and dispatch a second 2D sub-group of the 2D thread group to the compute block of the multiple compute blocks, the second 2D sub-group associated with a second 2D tile of memory.

Abstract: Embodiments are generally directed to memory prefetching in multiple GPU environment. An embodiment of an apparatus includes multiple processors including a host processor and multiple graphics processing units (GPUs) to process data, each of the GPUs including a prefetcher and a cache; and a memory for storage of data, the memory including a plurality of memory elements, wherein the prefetcher of each of the GPUs is to prefetch data from the memory to the cache of the GPU; and wherein the prefetcher of a GPU is prohibited from prefetching from a page that is not owned by the GPU or by the host processor.

Abstract: Systems, apparatuses and methods may provide away to render augmented reality (AR) and/or virtual reality (VR) sensory enhancements using ray tracing. More particularly, systems, apparatuses and methods may provide a way to normalize environment information captured by multiple capture devices, and calculate, for an observer, the sound sources or sensed events vector paths. The systems, apparatuses and methods may detect and/or manage one or more capture devices and assign one or more the capture devices based on one or more conditions to provide observer an immersive VR/AR experience.

Abstract: An apparatus to facilitate compression of memory data is disclosed. The apparatus comprises one or more processors to receive uncompressed data, adapt a format of the uncompressed data to a compression format, perform a color transformation from a first color space to a second color space, perform a residual computation to generate residual data, compress the residual data via entropy encoding to generate compressed data and packing the compressed data.

Abstract: Embodiments described herein provide a technique to facilitate the broadcast or multicast of asynchronous loads to shared local memory of a plurality of graphics cores within a graphics core cluster. One embodiment provides a graphics processor including a cache memory a graphics core cluster coupled with the cache memory. The graphics core cluster includes a plurality of graphics cores. The plurality of graphics cores includes a graphics core configured to receive a designation as a producer graphics core for a multicast load, read data from the cache memory; and transmit the data read from the cache memory to a consumer graphics core of the plurality of graphics cores.