公开(公告)号：US20170358129A1

公开(公告)日：2017-12-14

申请号：US15525023

申请日：2014-12-08

Applicant: Intel Corporation

Inventor： Feng Chen ,  Yi Yang ,  Xiaoming Chen

IPC: G06T15/80 ,  G06T1/20

CPC classification number: G06T15/80 ,  G06F9/30014 ,  G06F9/3861 ,  G06T1/20 ,  G06T15/005

Abstract: One or more system, apparatus, method, and computer readable media is described below for automated data type precision control capable of improving rendering quality on a graphics processor. Perceptible rendering quality is dependent at least in part on number format precision (e.g., FP16 or FP32) employed for shader program variables. In accordance with embodiments, shader variables implemented in lower precision data formats are tracked during shader compile to identify those that might trigger a floating point overflow and/or underflow exception. For shaders including one or more such variable, resources are provided to automatically monitor overflow and/or underflow exceptions during shader execution. In further embodiments, shader code is automatically re-generated based, at least in part, upon occurrences of such exceptions, and an increased number format precision specified for one or more of the tracked shader variables.

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.

公开(公告)号：US12039331B2

公开(公告)日：2024-07-16

申请号：US17967283

申请日：2022-10-17

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 ,  G06F7/483 ,  G06F7/544 ,  G06F9/38 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G09G5/393 ,  G06F1/16 ,  G06N20/00 ,  G06T15/00

CPC classification number: G06F9/3001 ,  G06F7/483 ,  G06F7/5443 ,  G06F9/30014 ,  G06F9/30036 ,  G06F9/3851 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G09G5/393 ,  G06F1/16 ,  G06F9/30025 ,  G06F9/3013 ,  G06F2207/3824 ,  G06N20/00 ,  G06T15/005

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.

公开(公告)号：US12001944B2

公开(公告)日：2024-06-04

申请号：US17874876

申请日：2022-07-27

Applicant: Intel Corporation

Inventor： Rajkishore Barik ,  Brian T. Lewis ,  Murali Sundaresan ,  Jeffrey Jackson ,  Feng Chen ,  Xiaoming Chen ,  Mike Macpherson

IPC: G06N3/063 ,  G06F9/46 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06N5/01

CPC classification number: G06N3/063 ,  G06F9/46 ,  G06N3/044 ,  G06N3/045 ,  G06N3/084 ,  G06N5/01

Abstract: A mechanism is described for facilitating smart distribution of resources for deep learning autonomous machines. A method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and introducing a library to a neural network application to determine an optimal point at which to apply frequency scaling without degrading performance of the neural network application at a computing device.

公开(公告)号：US20240004829A1

公开(公告)日：2024-01-04

申请号：US18350902

申请日：2023-07-12

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 ,  G06N3/063 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045 ,  G06N3/048

CPC classification number: G06F15/8007 ,  G06F13/4027 ,  G06T1/20 ,  G06F9/3004 ,  G06F13/00 ,  G06N3/063 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045 ,  G06N3/048

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：US11797837B2

公开(公告)日：2023-10-24

申请号：US15494971

申请日：2017-04-24

Applicant: Intel Corporation

Inventor： Altug Koker ,  Abhishek R. Appu ,  Kamal Sinha ,  Joydeep Ray ,  Balaji Vembu ,  Elmoustapha Ould-Ahmed-Vall ,  Sara S. Baghsorkhi ,  Anbang Yao ,  Kevin Nealis ,  Xiaoming Chen ,  John C. Weast ,  Justin E. Gottschlich ,  Prasoonkumar Surti ,  Chandrasekaran Sakthivel ,  Farshad Akhbari ,  Nadathur Rajagopalan Satish ,  Liwei Ma ,  Jeremy Bottleson ,  Eriko Nurvitadhi ,  Travis T. Schluessler ,  Ankur N. Shah ,  Jonathan Kennedy ,  Vasanth Ranganathan ,  Sanjeev Jahagirdar

IPC: G06N3/08 ,  G06N20/00 ,  G06N3/063 ,  G06N3/044 ,  G06N3/045 ,  G06N3/048

CPC classification number: G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N20/00 ,  G06N3/048

Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.

公开(公告)号：US20230334316A1

公开(公告)日：2023-10-19

申请号：US18314450

申请日：2023-05-09

Applicant: Intel Corporation

Inventor： Altug Koker ,  Abhishek R. Appu ,  Kamal Sinha ,  Joydeep Ray ,  Balaji Vembu ,  Elmoustapha Ould-Ahmed-Vall ,  Sara S. Baghsorkhi ,  Anbang Yao ,  Kevin Nealis ,  Xiaoming Chen ,  John C. Weast ,  Justin E. Gottschlich ,  Prasoonkumar Surti ,  Chandrasekaran Sakthivel ,  Farshad Akhbari ,  Nadathur Rajagopalan Satish ,  Liwei Ma ,  Jeremy Bottleson ,  Eriko Nurvitadhi ,  Travis T. Schluessler ,  Ankur N. Shah ,  Jonathan Kennedy ,  Vasanth Ranganathan ,  Sanjeev Jahagirdar

IPC: G06N3/08 ,  G06N20/00 ,  G06N3/063 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N20/00 ,  G06N3/063 ,  G06N3/044 ,  G06N3/045 ,  G06N3/048

Abstract: Described herein is a graphics processor comprising a memory device and a graphics processing cluster coupled with the memory device. The graphics processing cluster includes a plurality of graphics multiprocessors interconnected via a data interconnect. A graphics multiprocessor includes circuitry configured to load a modular neural network including a plurality of subnetworks, each of the plurality of subnetworks trained to perform a computer vision operation on a separate subject.

公开(公告)号：US11720355B2

公开(公告)日：2023-08-08

申请号：US17834482

申请日：2022-06-07

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 ,  G06N3/063 ,  G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G06T15/00 ,  G06N20/00 ,  G06F17/16

CPC classification number: G06F9/3001 ,  G06F7/483 ,  G06F7/5443 ,  G06F9/30014 ,  G06F9/30036 ,  G06F9/3851 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G09G5/393 ,  G06F9/3013 ,  G06F9/30025 ,  G06F17/16 ,  G06F2207/3824 ,  G06N20/00 ,  G06T15/005

Abstract: One embodiment provides a graphics processor comprising a memory controller and a graphics processing resource coupled with the memory controller. The graphics processing resource includes circuitry configured to execute an instruction to perform a matrix operation on first input including weight data and second input including input activation data, generate intermediate data based on a result of the matrix operation, quantize the intermediate data to a floating-point format determined based on a statistical distribution of first output data, and output, as second output data, quantized intermediate data in a determined floating-point format.

公开(公告)号：US20230046506A1

公开(公告)日：2023-02-16

申请号：US17967283

申请日：2022-10-17

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 ,  G06F7/483 ,  G06N3/063 ,  G06N3/04 ,  G06F9/38 ,  G06N3/08 ,  G09G5/393 ,  G06F7/544 ,  G06T15/00 ,  G06N20/00 ,  G06F17/16

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.