公开(公告)号：US11803935B2

公开(公告)日：2023-10-31

申请号：US17881720

申请日：2022-08-05

Applicant: Intel Corporation

Inventor： Eriko Nurvitadhi ,  Balaji Vembu ,  Tsung-Han Lin ,  Kamal Sinha ,  Rajkishore Barik ,  Nicolas C. Galoppo Von Borries

IPC: G06F17/16 ,  G06T1/20 ,  G06F9/30 ,  G06F9/38 ,  G06F12/0811 ,  G06F12/0815 ,  G06F12/0831 ,  G06F12/0888 ,  H03M7/30 ,  G06N20/00 ,  G06F12/02 ,  G06F18/2136 ,  G06F9/48 ,  G06N3/04 ,  G06N3/08 ,  G06T1/60 ,  G06T15/00

CPC classification number: G06T1/20 ,  G06F9/3001 ,  G06F9/3885 ,  G06F9/4881 ,  G06F12/0207 ,  G06F12/0811 ,  G06F12/0815 ,  G06F12/0831 ,  G06F12/0888 ,  G06F17/16 ,  G06F18/2136 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00 ,  G06T1/60 ,  G06T15/005 ,  H03M7/30 ,  G06F2212/1024 ,  G06F2212/302 ,  G06F2212/401 ,  G06F2212/621 ,  G06T2200/28

Abstract: Techniques to improve performance of matrix multiply operations are described in which a compute kernel can specify one or more element-wise operations to perform on output of the compute kernel before the output is transferred to higher levels of a processor memory hierarchy.

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：US20230297526A1

公开(公告)日：2023-09-21

申请号：US17832305

申请日：2022-06-03

Applicant: Intel Corporation

Inventor： David Puffer ,  Ankur Shah ,  Niranjan Cooray ,  Bryan White ,  Balaji Vembu ,  Hema Chand Nalluri ,  Kritika Bala

IPC: G06F13/24 ,  G06F13/16

CPC classification number: G06F13/24 ,  G06F13/1668 ,  G06T1/20

Abstract: Embodiments described herein provide techniques to facilitate scalable interrupts and workload submission for a virtualized graphics processor. The techniques include memory-based interrupt reporting and shared work queue submission for multiple software domains.

公开(公告)号：US11748283B1

公开(公告)日：2023-09-05

申请号：US17832305

申请日：2022-06-03

Applicant: Intel Corporation

Inventor： David Puffer ,  Ankur Shah ,  Niranjan Cooray ,  Bryan White ,  Balaji Vembu ,  Hema Chand Nalluri ,  Kritika Bala

IPC: G06F9/48 ,  G06F12/084 ,  G06F13/24 ,  G06F13/16 ,  G06T1/20

CPC classification number: G06F13/24 ,  G06F13/1668 ,  G06T1/20

Abstract: Embodiments described herein provide techniques to facilitate scalable interrupts and workload submission for a virtualized graphics processor. The techniques include memory-based interrupt reporting and shared work queue submission for multiple software domains.

公开(公告)号：US11748106B2

公开(公告)日：2023-09-05

申请号：US17683564

申请日：2022-03-01

Applicant: Intel Corporation

Inventor： Liwei Ma ,  Nadathur Rajagopalan Satish ,  Jeremy Bottleson ,  Farshad Akhbari ,  Eriko Nurvitadhi ,  Abhishek R. Appu ,  Altug Koker ,  Kamal Sinha ,  Joydeep Ray ,  Balaji Vembu ,  Vasanth Ranganathan ,  Sanjeev Jahagirdar

IPC: G06F9/38

CPC classification number: G06F9/3832

Abstract: A mechanism is described for facilitating fast data operations and for facilitating a finite state machine for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting input data to be used in computational tasks by a computation component of a processor including a graphics processor. The method may further include determining one or more frequently-used data values (FDVs) from the data, and pushing the one or more frequent data values to bypass the computational tasks.

公开(公告)号：US20230260072A1

公开(公告)日：2023-08-17

申请号：US18168207

申请日：2023-02-13

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

CPC classification number: G06T1/20 ,  G06F9/45533 ,  G06F9/5061 ,  G06F9/5094 ,  G06N3/063 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045 ,  G06F8/41

Abstract: 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.

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：US20230142472A1

公开(公告)日：2023-05-11

申请号：US17959374

申请日：2022-10-04

Applicant: Intel Corporation

Inventor： Eric J. Asperheim ,  Subramaniam Maiyuran ,  Kiran C. Veernapu ,  Sanjeev S. Jahagirdar ,  Balaji Vembu ,  Devan Burke ,  Philip R. Laws ,  Kamal Sinha ,  Abhishek R. Appu ,  Elmoustapha Ould-Ahmed-Vall ,  Peter L. Doyle ,  Joydeep Ray ,  Travis T. Schluessler ,  John H. Feit ,  Nikos Kaburlasos ,  Jacek Kwiatkowski ,  Altug Koker

IPC: G06F3/14 ,  G06F3/01 ,  G09G5/391 ,  G06F3/0484

CPC classification number: G06F3/1438 ,  G06F3/013 ,  G09G5/391 ,  G06F3/0484 ,  G09G2354/00 ,  G09G2352/00 ,  G09G2360/08 ,  G09G2340/0435 ,  G09G2360/121 ,  G09G5/001

Abstract: In accordance with some embodiments, the render rate is varied across and/or up and down the display screen. This may be done based on where the user is looking in order to reduce power consumption and/or increase performance. Specifically the screen display is separated into regions, such as quadrants. Each of these regions is rendered at a rate determined by at least one of what the user is currently looking at, what the user has looked at in the past and/or what it is predicted that the user will look at next. Areas of less focus may be rendered at a lower rate, reducing power consumption in some embodiments.

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.