公开(公告)号：US20190304053A1

公开(公告)日：2019-10-03

申请号：US16446265

申请日：2019-06-19

Applicant: Intel Corporation

Inventor： Elmoustapha Ould-Ahmed-Vall ,  Sara S. Baghsorkhi ,  Anbang Yao ,  Kevin Nealis ,  Xiaoming Chen ,  Altug Koker ,  Abhishek R. Appu ,  John C. Weast ,  Mike B. Macpherson ,  Dukhwan Kim ,  Linda L. Hurd ,  Ben J. Ashbaugh ,  Barath Lakshmanan ,  Liwei Ma ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland

IPC: G06T1/20 ,  G06N20/00 ,  G06F7/483

Abstract: Embodiments described herein provide a graphics processor that can perform a variety of mixed and multiple precision instructions and operations. One embodiment provides a streaming multiprocessor that can concurrently execute multiple thread groups, wherein the streaming multiprocessor includes a single instruction, multiple thread (SIMT) architecture and the streaming multiprocessor is to execute multiple threads for each of multiple instructions. The streaming multiprocessor can perform concurrent integer and floating-point operations and includes a mixed precision core to perform operations at multiple precisions.

公开(公告)号：US10417731B2

公开(公告)日：2019-09-17

申请号：US15494886

申请日：2017-04-24

Applicant: Intel Corporation

Inventor： Prasoonkumar Surti ,  Narayan Srinivasa ,  Feng Chen ,  Joydeep Ray ,  Ben J. Ashbaugh ,  Nicolas C. Galoppo Von Borries ,  Eriko Nurvitadhi ,  Balaji Vembu ,  Tsung-Han Lin ,  Kamal Sinha ,  Rajkishore Barik ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Altug Koker ,  Nadathur Rajagopalan Satish ,  Farshad Akhbari ,  Dukhwan Kim ,  Wenyin Fu ,  Travis T. Schluessler ,  Josh B. Mastronarde ,  Linda L. Hurd ,  John H. Feit ,  Jeffery S. Boles ,  Adam T. Lake ,  Karthik Vaidyanathan ,  Devan Burke ,  Subramaniam Maiyuran ,  Abhishek R. Appu

IPC: G06T1/20 ,  G06F9/455 ,  G06F9/50 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06F8/41

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a plurality of processing units each comprising a plurality of execution units (EUs), wherein the plurality of EUs comprise a first EU type and a second EU type.

公开(公告)号：US20190243764A1

公开(公告)日：2019-08-08

申请号：US16277267

申请日：2019-02-15

Applicant: Intel Corporation

Inventor： Chandrasekaran Sakthivel ,  Prasoonkumar Surti ,  John C. Weast ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Abhishek R. Appu ,  Nicolas C. Galoppo Von Borries ,  Joydeep Ray ,  Narayan Srinivasa ,  Feng Chen ,  Ben J. Ashbaugh ,  Rajkishore Barik ,  Tsung-Han Lin ,  Kamal Sinha ,  Eriko Nurvitadhi ,  Balaji Vembu ,  Altug Koker

IPC: G06F12/0837 ,  G06N20/00 ,  G06T1/20 ,  G06N3/08

CPC classification number: G06F12/0837 ,  G06F12/0815 ,  G06F2212/62 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/08 ,  G06N3/084 ,  G06N3/088 ,  G06N20/00 ,  G06T1/20

Abstract: In an example, an apparatus comprises a plurality of processing unit cores, a plurality of cache memory modules associated with the plurality of processing unit cores, and a machine learning model communicatively coupled to the plurality of processing unit cores, wherein the plurality of cache memory modules share cache coherency data with the machine learning model. Other embodiments are also disclosed and claimed.

公开(公告)号：US20190206020A1

公开(公告)日：2019-07-04

申请号：US16197821

申请日：2018-11-21

Applicant: Intel Corporation

Inventor： Elmoustapha Ould-Ahmed-Vall ,  Sara S. Baghsorkhi ,  Anbang Yao ,  Kevin Nealis ,  Xiaoming Chen ,  Altug Koker ,  Abhishek R. Appu ,  John C. Weast ,  Mike B. Macpherson ,  Dukhwan Kim ,  Linda L. Hurd ,  Ben J. Ashbaugh ,  Barath Lakshmanan ,  Liwei Ma ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland

IPC: G06T1/20 ,  G06F7/483 ,  G06N20/00

CPC classification number: G06T1/20 ,  G06F3/14 ,  G06F7/483 ,  G06F9/30014 ,  G06F9/30185 ,  G06F9/3863 ,  G06F9/5044 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/084 ,  G06N20/00 ,  G06T1/60 ,  G06T15/005

Abstract: One embodiment provides an accelerator module comprising a memory stack including multiple memory dies; a graphics processing unit (GPU) coupled with the memory stack via one or more memory controllers, the GPU including a plurality of multiprocessors having a single instruction, multiple thread (SIMT) architecture, the multiprocessors to execute at least one single instruction. The at least one single instruction is to cause at least a portion of the GPU to perform a floating point operation on input having differing precisions. The floating point operation is a two-dimensional matrix multiply and accumulate operation.

公开(公告)号：US10186011B2

公开(公告)日：2019-01-22

申请号：US15581182

申请日：2017-04-28

Applicant: Intel Corporation

Inventor： Eriko Nurvitadhi ,  Balaji Vembu ,  Nicolas C. Galoppo Von Borries ,  Rajkishore Barik ,  Tsung-Han Lin ,  Kamal Sinha ,  Nadathur Rajagopalan Satish ,  Jeremy Bottleson ,  Farshad Akhbari ,  Altug Koker ,  Narayan Srinivasa ,  Dukhwan Kim ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Feng Chen ,  Elmoustapha Ould-Ahmed-Vall ,  Kevin Nealis ,  Xiaoming Chen ,  Anbang Yao

IPC: G06T1/20 ,  G06N3/08 ,  G06N3/04

Abstract: One embodiment provides for a compute apparatus to perform machine learning operations, the compute apparatus comprising a decode unit to decode a single instruction into a decoded instruction, the decoded instruction to cause the compute apparatus to perform a complex machine learning compute operation.

公开(公告)号：US20180314249A1

公开(公告)日：2018-11-01

申请号：US15581124

申请日：2017-04-28

Applicant: Intel Corporation

Inventor： Abhishek R. Appu ,  John C. Weast ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Prasoonkumar Surti ,  Chandrasekaran Sakthivel ,  Altug Koker ,  Farshad Akhbari ,  Feng Chen ,  Dukhwan Kim ,  Narayan Srinivasa ,  Nadathur Rajagopalan Satish ,  Kamal Sinha ,  Joydeep Ray ,  Balaji Vembu ,  Mike B. Macpherson ,  Linda L. Hurd ,  Sanjeev Jahagirdar ,  Vasanth Ranganathan

IPC: G05D1/00 ,  G06N3/08 ,  G06N3/04 ,  G06N3/063

CPC classification number: G06F9/5016 ,  G06F9/5061

Abstract: A mechanism is described for facilitating storage management for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting one or more components associated with machine learning, where the one or more components include memory and a processor coupled to the memory, and where the processor includes a graphics processor. The method may further include allocating a storage portion of the memory and a hardware portion of the processor to a machine learning training set, where the storage and hardware portions are precise for implementation and processing of the training set.

公开(公告)号：US20180308203A1

公开(公告)日：2018-10-25

申请号：US15495081

申请日：2017-04-24

Applicant: Intel Corporation

Inventor： Abhishek R. Appu ,  Altug Koker ,  John C. Weast ,  Mike B. Macpherson ,  Dukhwan Kim ,  Linda L. Hurd ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Prasoonkumar Surti ,  Chandrasekaran Sakthivel ,  Joydeep Ray

IPC: G06T1/20 ,  G06N3/04 ,  H04L29/08

CPC classification number: G06T1/20 ,  G06F9/5061 ,  G06F9/5072 ,  G06N3/02 ,  G06N3/04 ,  H04L67/10

Abstract: A mechanism is described for facilitating sharing of data and compression expansion of models at autonomous machines. A method of embodiments, as described herein, includes detecting a first processor processing information relating to a neural network at a first computing device, where the first processor comprises a first graphics processor and the first computing device comprises a first autonomous machine. The method further includes facilitating the first processor to store one or more portions of the information in a library at a database, where the one or more portions are accessible to a second processor of a computing device.

公开(公告)号：US20180307983A1

公开(公告)日：2018-10-25

申请号：US15494948

申请日：2017-04-24

Applicant: Intel Corporation

Inventor： Narayan Srinivasa ,  Joydeep Ray ,  Nicolas C. Galoppo Von Borries ,  Ben Ashbaugh ,  Prasoonkumar Surti ,  Feng Chen ,  Barath Lakshmanan ,  Elmoustapha Ould-Ahmed-Vall ,  Liwei Ma ,  Linda L. Hurd ,  Abhishek R. Appu ,  John C. Weast ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Chandrasekaran Sakthivel ,  Farshad Akhbari ,  Dukhwan Kim ,  Altug Koker ,  Nadathur Rajagopalan Satish

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/082

Abstract: 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.

公开(公告)号：US20180300246A1

公开(公告)日：2018-10-18

申请号：US15489149

申请日：2017-04-17

Applicant: Intel Corporation

Inventor： Chandrasekaran Sakthivel ,  Prasoonkumar Surti ,  John C. Weast ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Abhishek R. Appu ,  Nicolas C. Galoppo Von Borries ,  Joydeep Ray ,  Narayan Srinivasa ,  Feng Chen ,  Ben J. Ashbaugh ,  Rajkishore Barik ,  Tsung-Han Lin ,  Kamal Sinha ,  Eriko Nurvitadhi ,  Balaji Vembu ,  Altug Koker

IPC: G06F12/0837 ,  G06N3/08 ,  G06N99/00

CPC classification number: G06F12/0837 ,  G06F2212/62 ,  G06N3/08 ,  G06N99/005 ,  G06T1/20

Abstract: In an example, an apparatus comprises a plurality of processing unit cores, a plurality of cache memory modules associated with the plurality of processing unit cores, and a machine learning model communicatively coupled to the plurality of processing unit cores, wherein the plurality of cache memory modules share cache coherency data with the machine learning model. Other embodiments are also disclosed and claimed.

公开(公告)号：US10025570B2

公开(公告)日：2018-07-17

申请号：US15278091

申请日：2016-09-28

Applicant: INTEL CORPORATION

Inventor： Sara S. Baghsorkhi ,  Christos Margiolas

IPC: G06F9/45 ,  G06F8/41

Abstract: In one example, a system for modifying applications to support incremental checkpoints can include logic to generate a dominator tree based on a control flow graph for source code, wherein the control flow graph and the dominator tree comprise a plurality of nodes corresponding to basic blocks of the source code. The processor can select a region based on a leaf node of the dominator tree, the region based on an instruction threshold, and insert a first set of commit instructions into the source code based on entry points into the region and insert a second set of commit instructions into the source code based on exit points from the region. The processor can update the dominator tree to exclude the selected region and compile the source code into an executable application, wherein the first set of commit instructions and the second set of commit instructions enable incremental checkpoints.