Intel Corporation

Prasoonkumar Surti

Abhishek R Appu

Joydeep Ray

Altug Koker

Balaji Vembu

Vasanth Ranganathan

Subramaniam M Maiyuran

Karthik Vaidyanathan

Kamal Sinha

Travis T Schluessler

Elmoustapha Ould-Ahmed-Vall

Murali Ramadoss

Michael Apodaca

Kiran Chand Veernapu

Adam T Lake

John H Feit

Linda L Hurd

Nikos Kaburlasos

Ben J Ashbaugh

Chandrasekaran Sakthivel

Abhishek Venkatesh

Wenyin Fu

Jonathan Kennedy

Justin E Gottschlich

Scott R Janus

David M Puffer

Atsuo Kuwahara

Lakshminarayanan Striramassarma

Nicolas C Galoppo Von Borries

Hugues Labbe

Sara S Baghsorkhi

Varghese George

Guei-Yuan Lueh

Ankur N Shah

Jeffery S Boles

Feng Chen

Dukhwan Kim

Narayan Srinivasa

Aravindh V Anantaraman

Jacek Kwiatkowski

Carsten Benthin

Mike MacPherson

Bhushan M Borole

Kai Xiao

Devan Burke

Gabor Liktor

Rajkishore Barik

Farshad Akhbari

Jill MacDonald Boyce

Sanjeev S Jahagirdar

John Charles Weast

Nadathur Rajagopalan Satish

Eriko Nurvitadhi

Stanley J Baran

Pattabhiraman K

Gokcen Cilingir

Tsung-Han Lin

Philip R Laws

Narayan Biswal

Deepak Shashidhar Vembar

Josh B Mastronarde

Nilesh V Shah

James M Holland

Sven Woop

Tomas G Akenine-Moller

Mike B Macpherson

Liwei Ma

Peter L Doyle

Ingo Wald

James A Valerio

Niranjan L Cooray

Tomer Bar-On

Selvakumar Panneer

John G Gierach

Aditya Navale

Thomas A Piazza

Christopher J Hughes

Carl S Marshall

Carl Jacob Munkberg

Karthik Veeramani

Rajesh Poornachandran

Lidong Xu

Jon N Hasselgren

Xiaoming Chen

Yi-Jen Chiu

Hong Jiang

Kun Tian

Jason Daniel Tanner

Vallabhajosyula S Somayazulu

Daniel Pohl

Larry D Seiler

Michael S Strickland

Glen J Anderson

Eric C Samson

Robert M Toth

Zhi Hu Wang

Alexander F Heinecke

Anbang Yao

Zhi Jun Wang

Dan Baum

A system includes a compression engine that stores the compression format information embedded in the compressed data. The compression format information can be included in a header that includes compression control surface (CCS) information. The system includes a shared memory to store compressed data for multiple hardware pipelines, where blocks of the compressed data have a common memory footprint and the compression header. The compression engine can compress data to store in the shared memory including generation of the header. The compression engine can decompress data read from the shared memory, including identification of the compression format from the header.

Unified stateless compression system for universally consumable compression

A mechanism is described for detecting, at training time, information related to one or more tasks to be performed by the one or more processors according to a training dataset for a neural network, analyzing the information to determine one or more portions of hardware of a processor of the one or more processors that is configurable to support the one or more tasks, configuring the hardware to pre-select the one or more portions to perform the one or more tasks, while other portions of the hardware remain available for other tasks, and monitoring utilization of the hardware via a hardware unit of the graphics processor and, via a scheduler of the graphics processor, adjusting allocation of the one or more tasks to the one or more portions of the hardware based on the utilization.

Coordination and increased utilization of graphics processors during inference

For example, one embodiment of an apparatus comprises: ray traversal hardware logic to perform traversal operations to traverse rays through a bounding volume hierarchy (BVH) comprising a plurality of BVH nodes, the ray traversal hardware logic comprising a plurality of traversal storage banks to store traversal data associated with the BVH nodes and/or the rays as the ray traversal hardware logic performs the traversal operations; and a cache comprising a plurality of cache banks to store the traversal data prior to being moved into the traversal storage banks for processing by the ray traversal hardware logic; and an inter-bank interconnect comprising: a point-to-point switch matrix to couple any of the cache banks to any of the traversal storage banks; an arbiter/allocator to control the point-to-point switch matrix to establish a particular group of interconnections between the cache banks and the traversal storage banks in a given clock cycle.

Apparatus and method for routing data from ray tracing cache banks

Embodiments are generally directed to compression for compression for sparse data structures utilizing mode search approximation. An embodiment of an apparatus includes one or more processors including a graphics processor to process data; and a memory for storage of data, including compressed data. The one or more processors are to provide for compression of a data structure, including identification of a mode in the data structure, the data structure including a plurality of values and the mode being a most repeated value in a data structure, wherein identification of the mode includes application of a mode approximation operation, and encoding of an output vector to include the identified mode, a significance map to indicate locations at which the mode is present in the data structure, and remaining uncompressed data from the data structure.

Compression for sparse data structures utilizing mode search approximation

An apparatus to facilitate a dynamically scalable and partitioned copy engine is disclosed. The apparatus includes a processor comprising copy engine hardware circuitry to facilitate copying surface data in memory and comprising: a plurality of copy front-end hardware circuitry to generate a plurality of surface data sub-blocks, wherein a number of the plurality of copy front-end hardware circuitry corresponds to a number of partitions configured for the processor, with each partition associated with a single copy front-end hardware circuitry; a plurality of copy back-end hardware circuitry to operate in parallel to process the plurality of surface data sub-blocks to perform memory accesses, wherein subsets of the plurality of copy back-end hardware circuitry are each associated with the single copy front-end hardware circuitry associated with each partition; and a connectivity matrix hardware circuitry to communicably connect the plurality of copy front-end hardware circuitry to the plurality of copy back-end hardware circuitry.

Dynamically scalable and partitioned copy engine

Described herein is a data processing system having a multisample antialiasing compressor coupled to a texture unit and shader execution array. In one embodiment, the data processing system includes a memory device to store a multisample render target, the multisample render target to store color data for a set of sample locations of each pixel in a set of pixels; and general-purpose graphics processor comprising a multisample antialiasing compressor to apply multisample antialiasing compression to color data generated for the set of sample locations of a first pixel in the set of pixels and a multisample render cache to store color data generated for the set of sample locations of the first pixel in the set of pixels, wherein color data evicted from the multisample render cache is to be stored to the multisample render target.

Lossless compression for multisample render targets alongside fragment compression

An apparatus and method are described for performing an early depth test on graphics data. For example, one embodiment of a graphics processing apparatus comprises: early depth test circuitry to perform an early depth test on blocks of pixels to determine whether all pixels in the block of pixels can be resolved by the early depth test; a plurality of execution circuits to execute pixel shading operations on the blocks of pixels; and a scheduler circuit to schedule the blocks of pixels for the pixel shading operations, the scheduler circuit to prioritize the blocks of pixels in accordance with the determination as to whether all pixels in the block of pixels can be resolved by the early depth test.

Virtual reality apparatus and method including prioritized pixel shader operations, alternate eye rendering, and/or augmented timewarp

Examples described herein relate to a manner of determining a number of bits to encode compression data. Some examples include: compressing pixel data of a region of pixels in a frame; determining a number of bits associated with at least two partitions; utilizing the determined number of bits to encode residual values generated from the compressing the pixel data; and storing the encoded residual values. In some examples, the at least two partitions comprise a first partition and a second partition. Some examples include: encoding residuals in the first partition using a number of bits associated with the first partition and encoding residuals in the second partition using a number of bits associated with the second partition. Some examples include: determining a distribution of bins of residuals, wherein each different bin represents a number of bits used to encode a residual value and determining a midpoint of a total number of residuals as a bin that stores a residual that is approximately 50 percentile of the total number of residuals of the distribution.

Memory allocation technologies for data compression and de-compression

A mechanism is described for facilitating adaptive resolution and viewpoint-prediction for immersive media in computing environments. An apparatus of embodiments, as described herein, includes one or more processors to receive viewing positions associated with a user with respect to a display, and analyze relevance of media contents based on the viewing positions, where the media content includes immersive videos of scenes captured by one or more cameras. The one or more processors are further to predict portions of the media contents as relevant portions based on the viewing positions and transmit the relevant portions to be rendered and displayed.

Adaptive resolution of point cloud and viewpoint prediction for video streaming in computing environments

An apparatus to facilitate optimization of a neural network (NN) is disclosed. The apparatus includes optimization logic to define a NN topology having one or more macro layers, adjust the one or more macro layers to adapt to input and output components of the NN and train the NN based on the one or more macro layers.

Neural network optimization mechanism

In an example, an apparatus comprises a plurality of execution units, and a first memory communicatively couple to the plurality of execution units, wherein the first shared memory is shared by the plurality of execution units and a copy engine to copy context state data from at least a first of the plurality of execution units to the first shared memory. Other embodiments are also disclosed and claimed.

Engine to enable high speed context switching via on-die storage

Embodiments described herein provide techniques to enable the dynamic reconfiguration of memory on a general-purpose graphics processing unit. One embodiment described herein enables dynamic reconfiguration of cache memory bank assignments based on hardware statistics. One embodiment enables for virtual memory address translation using mixed four kilobyte and sixty-four kilobyte pages within the same page table hierarchy and under the same page directory. One embodiment provides for a graphics processor and associated heterogenous processing system having near and far regions of the same level of a cache hierarchy.

Dynamic memory reconfiguration

Embodiments described herein provided for an instruction and associated logic to enable a processing resource including a tensor accelerator to perform optimized computation of sparse submatrix operations. One embodiment provides a parallel processor comprising a processing cluster coupled with the cache memory. The processing cluster includes a plurality of multiprocessors coupled with a data interconnect, where a multiprocessor of the plurality of multiprocessors includes a tensor core configured to load tensor data and metadata associated with the tensor data from the cache memory, wherein the metadata indicates a first numerical transform applied to the tensor data, perform an inverse transform of the first numerical transform, perform a tensor operation on the tensor data after the inverse transform is performed, and write output of the tensor operation to a memory coupled with the processing cluster.

Systolic arithmetic on sparse data

Methods, systems and apparatuses may provide for technology that identifies that a first thread group of a pixel location is associated with a first shading rate, identifies that a second thread group of the pixel location is associated with a second shading rate, wherein the first shading rate is different from the second shading rate. The technology marks a dependency of the first thread group on the second thread group.

Dynamic dependency scoreboard for overlapping pixels of primitives

Apparatus and method for stable and short latency sorting. For example, one embodiment of a processor comprises: an input circuit to receive a set of N input values to be sorted into a sorted order; comparison circuitry to compare each input value with all other input values in parallel to generate at least N*(N−1)/2 comparison result values; matrix generation circuitry and/or logic to generate a result matrix having a row associated with each input value, a plurality of bits in each row comprising comparison result values indicating results of comparisons with other input values, wherein a first region of the result matrix is to store a first set of bits comprising the N*(N−1)/2 comparison result values and a second region of the result matrix, opposite the first region, is to store a second set of bits comprising an inverse of the N*(N−1)/2 comparison result values; a parallel adder circuit to perform parallel additions of the bits in each row to generate N unique result values; and sorting circuitry to index into the N unique result values to return the sorted order.

Apparatus and method for performing a stable and short latency sorting operation

Growing community of inventors

Folsom, CA, United States of America

Prasoonkumar Surti