Intel Corporation

Ben J Ashbaugh

Joydeep Ray

Altug Koker

Abhishek R Appu

Elmoustapha Ould-Ahmed-Vall

Prasoonkumar Surti

Nicolas C Galoppo Von Borries

Linda L Hurd

Sara S Baghsorkhi

Balaji Vembu

Kamal Sinha

Liwei Ma

Subramaniam M Maiyuran

Dukhwan Kim

Feng Chen

Michael S Strickland

Barath Lakshmanan

Narayan Srinivasa

Justin E Gottschlich

Murali Ramadoss

Xiaoming Chen

Rajkishore Barik

Anbang Yao

Ping Tak Tang

John Charles Weast

Eriko Nurvitadhi

Aravindh V Anantaraman

Tsung-Han Lin

Vasanth Ranganathan

Chandrasekaran Sakthivel

Mike MacPherson

Travis T Schluessler

Varghese George

Nadathur Rajagopalan Satish

Mike B Macpherson

Farshad Akhbari

Vikranth Vemulapalli

Jonathan D Pearce

Kevin Nealis

Tatiana Shpeisman

Jeffery S Boles

Josh B Mastronarde

Valentin Andrei

Karthik Vaidyanathan

Adam T Lake

John H Feit

Devan Burke

Wenyin Fu

Sungye Kim

William B Sadler

Lakshminarayanan Striramassarma

Ankur N Shah

Scott R Janus

James A Valerio

Brandon L Fliflet

Brent E Insko

Arthur Hunter, Jr

Jingyi Jin

Aditya Navale

Marian Alin Petre

Rajesh Poornachandran

Pattabhiraman K

Tomasz Janczak

Oguz Hasan Elibol

Anil Thomas

Jayaram Bobba

Kumar Balasubramanian

Jerin C Justin

Gokce Keskin

Pradeep Ramani

Rama Harihara

Sanjeev S Jahagirdar

Ali-Reza Adl-Tabatabai

Michael Apodaca

Selvakumar Panneer

Hema Chand Nalluri

Javier Sebastian Turek

Brian T Lewis

Jeremy Bottleson

Jayanth N Rao

Yang Ni

Dhawal Srivastava

Saurabh Tangri

Lindsey Kuper

Naila Farooqui

David M Puffer

Yoav Harel

Ronald W Silvas

Michal Andrzej Mrozek

Ravishankar Iyer

Eric C Samson

Peter L Doyle

Niranjan L Cooray

Wei-Yu Chen

Nilesh K Jain

Srinivasan Embar Raghukrishnan

Rahul Vijay Kulkarni

Joseph Koston

Dmitry Babokin

Alan M Curtis

Described herein is a general-purpose graphics processing unit including a multiprocessor having a single instruction, multiple thread, SIMT, architecture. The multiprocessor comprises multiple sets of compute units each having a first logic unit configured to perform floating-point operations and a second logic unit configured to perform integer operations, with a thread of the floating-point instruction being executed in parallel with a thread of the integer instruction.

Multicore processor with each core having independent floating point datapath and integer datapath

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

One embodiment provides a general-purpose graphics processing unit comprising a dynamic precision floating-point unit including a control unit having precision tracking hardware logic to track an available number of bits of precision for computed data relative to a target precision, wherein the dynamic precision floating-point unit includes computational logic to output data at multiple precisions.

Compute optimizations for low precision machine learning operations

An apparatus to facilitate processing in a multi-tile device is disclosed. The apparatus comprises a plurality of processing tiles, each including a memory device and a plurality of processing resources, coupled to the device memory, and a memory management unit to manage the memory devices in each of the plurality of tiles to perform allocation of memory resources among the memory devices for execution by the plurality of processing resources.

Multi-tile graphics processing unit

Embodiments described herein provide techniques to facilitate instruction-based control of memory attributes. One embodiment provides a graphics processor comprising a processing resource, a memory device, a cache coupled with the processing resources and the memory, and circuitry to process a memory access message received from the processing resource. The memory access message enables access to data of the memory device. To process the memory access message, the circuitry is configured to determine one or more cache attributes that indicate whether the data should be read from or stored the cache. The cache attributes may be provided by the memory access message or stored in state data associated with the data to be accessed by the access message.

Implicit fence for write messages

Systems and methods for supporting generic pointers in hardware of a graphics processing unit (GPU) are provided. In various examples, a GPU includes multiple sub-cores each having a processing resource and a load/store pipeline. The processing resource is operable to receive a memory access message including a pointer and a memory type identifier indicative of the pointer representing a generic pointer. The processing resource is further operable to output a load or store operation to the load/store pipeline based on the memory access message, including computing an address for the load or store operation by adding a base address of a named memory type of a plurality of named memory types referenced by the generic pointer to an offset into a memory of the named memory type. The load/store pipeline is operable to, responsive to receipt of the load or store operation, access the memory at the address.

Providing native support for generic pointers in a graphics processing unit

An apparatus to facilitate processing in a multi-tile device is disclosed. In one embodiment, the apparatus includes a graphics processor comprising a first semiconductor die including a first high-bandwidth memory (HBM) device, a second semiconductor die including a second HBM device, and a third semiconductor die coupled with the first semiconductor die and the second semiconductor die in a 2.5-dimensional (2.5D) arrangement. The third semiconductor die includes a graphics processing resource and a cache coupled with the graphics processing resource. The cache is configurable to cache data associated with memory accessed by the graphics processing resource and the graphics processing resource includes a general-purpose graphics processor core and a tensor core.

Systems and methods for improving cache efficiency and utilization are disclosed. In one embodiment, a graphics processor includes processing resources to perform graphics operations and a cache controller of a cache coupled to the processing resources. The cache controller is configured to control cache priority by determining whether default settings or an instruction will control cache operations for the cache.

Systems and methods for improving cache efficiency and utilization

Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.

Compute optimization mechanism for deep neural networks

One embodiment provides for a graphics processing unit (GPU) to accelerate machine learning operations, the GPU comprising an instruction cache to store a first instruction and a second instruction, the first instruction to cause the GPU to perform a floating-point operation, including a multi-dimensional floating-point operation, and the second instruction to cause the GPU to perform an integer operation; and a general-purpose graphics compute unit having a single instruction, multiple thread architecture, the general-purpose graphics compute unit to concurrently execute the first instruction and the second instruction.

Concurrent multi-datatype execution within a processing resource

An apparatus to facilitate barrier state save and restore for preemption in a graphics environment is disclosed. The apparatus includes processing resources to execute a plurality of execution threads that are comprised in a thread group (TG) and mid-thread preemption barrier save and restore hardware circuitry to: initiate an exception handling routine in response to a mid-thread preemption event, the exception handling routine to cause a barrier signaling event to be issued; receive indication of a valid designated thread status for a thread of a thread group (TG) in response to the barrier signaling event; and in response to receiving the indication of the valid designated thread status for the thread of the TG, cause, by the thread of the TG having the valid designated thread status, a barrier save routine and a barrier restore routine to be initiated for named barriers of the TG.

Barrier state save and restore for preemption in a graphics environment

One embodiment provides a multi-chip module accelerator usable to execute tensor data processing operations a multi-chip module. The multi-chip module may include a memory stack including multiple memory dies and parallel processor circuitry communicatively coupled to the memory stack. The parallel processor circuitry may include multiprocessor cores to execute matrix multiplication and accumulate operations. The matrix multiplication and accumulate operations may include floating-point operations that are configurable to include two-dimensional matrix multiply and accumulate operations involving inputs that have differing floating-point precisions. The floating-point operations may include a first operation at a first precision and a second operation at a second precision. The first operation may include a multiply having at least one 16-bit floating-point input and the second operation may include an accumulate having a 32-bit floating-point input.

Methods and apparatus relating to scalar core integration in a graphics processor. In an example, an apparatus comprises a processor to receive a set of workload instructions for a graphics workload from a host complex, determine a first subset of operations in the set of operations that is suitable for execution by a scalar processor complex of the graphics processing device and a second subset of operations in the set of operations that is suitable for execution by a vector processor complex of the graphics processing device, assign the first subset of operations to the scalar processor complex for execution to generate a first set of outputs, assign the second subset of operations to the vector processor complex for execution to generate a second set of outputs. Other embodiments are also disclosed and claimed.

Scalar core integration

Embodiments described herein include software, firmware, and hardware that provides techniques to enable deterministic scheduling across multiple general-purpose graphics processing units. One embodiment provides a multi-GPU architecture with uniform latency. One embodiment provides techniques to distribute memory output based on memory chip thermals. One embodiment provides techniques to enable thermally aware workload scheduling. One embodiment provides techniques to enable end to end contracts for workload scheduling on multiple GPUs.

Graphics processor operation scheduling for deterministic latency

Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.

Compression of machine learning models utilizing pseudo-labeled data training

Growing community of inventors

Folsom, CA, United States of America

Ben J Ashbaugh