公开(公告)号：US11080811B2

公开(公告)日：2021-08-03

申请号：US16446398

申请日：2019-06-19

Applicant: Intel Corporation

Inventor： Abhishek R. Appu ,  Altug Koker ,  Linda L. Hurd ,  Dukhwan Kim ,  Mike B. Macpherson ,  John C. Weast ,  Feng Chen ,  Farshad Akhbari ,  Narayan Srinivasa ,  Nadathur Rajagopalan Satish ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland ,  Xiaoming Chen ,  Anbang Yao ,  Tatiana Shpeisman

IPC: G06T1/20 ,  G06F3/14 ,  G06F9/30 ,  G06F9/38 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06T15/00 ,  G09G5/36 ,  G06T15/04

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core to perform a mixed precision multi-dimensional matrix multiply and accumulate operation on 16-bit and/or 32 bit floating-point elements.

公开(公告)号：US20210124579A1

公开(公告)日：2021-04-29

申请号：US17115989

申请日：2020-12-09

Applicant: Intel Corporation

Inventor： Himanshu Kaul ,  Mark A. Anders ,  Sanu K. Mathew ,  Anbang Yao ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland ,  Xiaoming Chen ,  Tatiana Shpeisman ,  Abhishek R. Appu ,  Altug Koker ,  Kamal Sinha ,  Balaji Vembu ,  Nicolas C. Galoppo Von Borries ,  Eriko Nurvitadhi ,  Rajkishore Barik ,  Tsung-Han Lin ,  Vasanth Ranganathan ,  Sanjeev Jahagirdar

IPC: G06F9/30 ,  G09G5/393 ,  G06F9/38 ,  G06F7/483 ,  G06F7/544 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08

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 a 32-bit intermediate product of 16-bit operands and to compute a 32-bit sum based on the 32-bit intermediate product.

公开(公告)号：US10726514B2

公开(公告)日：2020-07-28

申请号：US15581167

申请日：2017-04-28

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 ,  G06N3/08 ,  G06F9/30 ,  G06N3/04 ,  G06N3/063 ,  G06F9/50 ,  G06F9/38 ,  G06N20/00 ,  G06F3/14 ,  G06T1/60 ,  G06T15/00

Abstract: 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.

公开(公告)号：US20200226096A1

公开(公告)日：2020-07-16

申请号：US16744407

申请日：2020-01-16

Applicant: Intel Corporation

Inventor： Altug Koker ,  Farshad Akhbari ,  Feng Chen ,  Dukhwan Kim ,  Narayan Srinivasa ,  Nadathur Rajagopalan Satish ,  Liwei Ma ,  Jeremy Bottleson ,  Eriko Nurvitadhi ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland ,  Xiaoming Chen ,  Tatiana Shpeisman ,  Abhishek R. Appu

IPC: G06F15/80 ,  G06F13/40 ,  G06T1/20 ,  G06F9/30 ,  G06F13/00 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08

Abstract: An integrated circuit (IC) package apparatus is disclosed. The IC package includes one or more processing units and a bridge, mounted below the one or more processing unit, including one or more arithmetic logic units (ALUs) to perform atomic operations.

公开(公告)号：US10474458B2

公开(公告)日：2019-11-12

申请号：US15787129

申请日：2017-10-18

Applicant: Intel Corporation

Inventor： Himanshu Kaul ,  Mark A. Anders ,  Sanu K. Mathew ,  Anbang Yao ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland ,  Xiaoming Chen ,  Tatiana Shpeisman ,  Abhishek R. Appu ,  Altug Koker ,  Kamal Sinha ,  Balaji Vembu ,  Nicolas C. Galoppo Von Borries ,  Eriko Nurvitadhi ,  Rajkishore Barik ,  Tsung-Han Lin ,  Vasanth Ranganathan ,  Sanjeev Jahagirdar

IPC: G09G5/00 ,  G06F9/30 ,  G09G5/393 ,  G06F9/38 ,  G06F7/483 ,  G06F7/544 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06T15/00 ,  G06N20/00

Abstract: One embodiment provides for a machine-learning hardware accelerator comprising a compute unit having an adder and a multiplier that are shared between integer data path and a floating-point datapath, the upper bits of input operands to the multiplier to be gated during floating-point operation.

公开(公告)号：US20190304054A1

公开(公告)日：2019-10-03

申请号：US16446398

申请日：2019-06-19

Applicant: Intel Corporation

Inventor： Abhishek R. Appu ,  Altug Koker ,  Linda L. Hurd ,  Dukhwan Kim ,  Mike B. Macpherson ,  John C. Weast ,  Feng Chen ,  Farshad Akhbari ,  Narayan Srinivasa ,  Nadathur Rajagopalan Satish ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland ,  Xiaoming Chen ,  Anbang Yao ,  Tatiana Shpeisman

IPC: G06T1/20 ,  G06N3/08 ,  G06N3/04 ,  G06T15/00 ,  G09G5/36 ,  G06F9/30 ,  G06F3/14 ,  G06N3/063 ,  G06F9/38

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core to perform a mixed precision multi-dimensional matrix multiply and accumulate operation on 16-bit and/or 32 bit floating-point elements.

公开(公告)号：US20190188554A1

公开(公告)日：2019-06-20

申请号：US16283021

申请日：2019-02-22

Applicant: Intel Corporation

Inventor： Liwei Ma ,  Elmoustapha Ould-Ahmed-Vall ,  Barath Lakshmanan ,  Ben J. Ashbaugh ,  Jingyi Jin ,  Jeremy Bottleson ,  Mike B. Macpherson ,  Kevin Nealis ,  Dhawal Srivastava ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland ,  Xiaoming Chen ,  Anbang Yao ,  Tatiana Shpeisman ,  Altug Koker ,  Abhishek R. Appu

IPC: G06N3/04 ,  G06N3/08 ,  G06T1/20 ,  G06N3/063

CPC classification number: G06N3/04 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/082 ,  G06T1/20

Abstract: Embodiments provide systems and methods which facilitate optimization of a convolutional neural network (CNN). One embodiment provides for a non-transitory machine-readable medium storing instructions that cause one or more processors to perform operations comprising processing a trained convolutional neural network (CNN) to generate a processed CNN, the trained CNN having weights in a floating-point format. Processing the trained CNN includes quantizing the weights in the floating-point format to generate weights in an integer format. Quantizing the weights includes generating a quantization table to enable non-uniform quantization of the weights and quantizing the weights from the floating-point format to the integer format using the quantization table. The operations additionally comprise performing an inference operation utilizing the processed CNN with the integer format weights.

公开(公告)号：US20180315159A1

公开(公告)日：2018-11-01

申请号：US15789565

申请日：2017-10-20

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 ,  G06N99/00 ,  G06F7/483

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 ,  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 to cause at least a portion of the GPU to perform a floating-point operation on input having differing precisions; and the floating-point operation is a two-dimensional matrix multiply and accumulate operation.

公开(公告)号：US12056788B2

公开(公告)日：2024-08-06

申请号：US17684187

申请日：2022-03-01

Applicant: Intel Corporation

Inventor： Abhishek R. Appu ,  Altug Koker ,  Linda L. Hurd ,  Dukhwan Kim ,  Mike B. Macpherson ,  John C. Weast ,  Feng Chen ,  Farshad Akhbari ,  Narayan Srinivasa ,  Nadathur Rajagopalan Satish ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland ,  Xiaoming Chen ,  Anbang Yao ,  Tatiana Shpeisman

IPC: G06T1/20 ,  G06F3/14 ,  G06F9/30 ,  G06F9/38 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084 ,  G06T15/00 ,  G09G5/36 ,  G06T15/04

CPC classification number: G06T1/20 ,  G06F3/14 ,  G06F9/3001 ,  G06F9/30014 ,  G06F9/3017 ,  G06F9/3887 ,  G06F9/3895 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084 ,  G06T15/005 ,  G09G5/363 ,  G06F9/3851 ,  G06T15/04 ,  G09G2360/06 ,  G09G2360/08 ,  G09G2360/121

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core including mixed-precision execution circuitry to execute one or more of the mixed-precision instructions to perform a mixed-precision dot-product operation comprising to perform a set of multiply and accumulate operations.

公开(公告)号：US12050984B2

公开(公告)日：2024-07-30

申请号：US17083080

申请日：2020-10-28

Applicant: Intel Corporation

Inventor： Rajkishore Barik ,  Elmoustapha Ould-Ahmed-Vall ,  Xiaoming Chen ,  Dhawal Srivastava ,  Anbang Yao ,  Kevin Nealis ,  Eriko Nurvitadhi ,  Sara S. Baghsorkhi ,  Balaji Vembu ,  Tatiana Shpeisman ,  Ping T. Tang

IPC: G06N3/06 ,  G06F9/30 ,  G06F9/38 ,  G06F16/17 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084 ,  G06T1/20

CPC classification number: G06N3/063 ,  G06F9/3001 ,  G06F9/3017 ,  G06F9/3851 ,  G06F9/3887 ,  G06F9/3895 ,  G06F16/17 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06T1/20

Abstract: One embodiment provides for a general-purpose graphics processing unit including a scheduler to schedule multiple matrix operations for execution by a general-purpose graphics processing unit. The multiple matrix operations are determined based on a single machine learning compute instruction. The single machine learning compute instruction is a convolution instruction and the multiple matrix operations are associated with a convolution operation.