公开(公告)号：US20250166115A1

公开(公告)日：2025-05-22

申请号：US18971949

申请日：2024-12-06

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 ,  G06F8/41 ,  G06F9/455 ,  G06F9/50 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084

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：US20250094170A1

公开(公告)日：2025-03-20

申请号：US18901027

申请日：2024-09-30

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 ,  G06F1/16 ,  G06F7/483 ,  G06F7/544 ,  G06F9/38 ,  G06F17/16 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N20/00 ,  G06T15/00 ,  G09G5/393

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：US20250046001A1

公开(公告)日：2025-02-06

申请号：US18800406

申请日：2024-08-12

Applicant: Intel Corporation

Inventor： Prasoonkumar Surti ,  Arthur Hunter ,  Kamal Sinha ,  Scott Janus ,  Brent Insko ,  Vasanth Ranganathan ,  Lakshminarayanan Striramassarma

IPC: G06T15/00 ,  G06T1/20 ,  G06T1/60 ,  G06T17/20

Abstract: Embodiments are generally directed to multi-tile graphics processor rendering. An embodiment of an apparatus includes a memory for storage of data; and one or more processors including a graphics processing unit (GPU) to process data, wherein the GPU includes a plurality of GPU tiles, wherein, upon geometric data being assigned to each of a plurality of screen tiles, the apparatus is to transfer the geometric data to the plurality of GPU tiles.

公开(公告)号：US12210477B2

公开(公告)日：2025-01-28

申请号：US17428530

申请日：2020-03-14

Applicant: Intel Corporation

Inventor： Altug Koker ,  Joydeep Ray ,  Ben Ashbaugh ,  Jonathan Pearce ,  Abhishek Appu ,  Vasanth Ranganathan ,  Lakshminarayanan Striramassarma ,  Elmoustapha Ould-Ahmed-Vall ,  Aravindh Anantaraman ,  Valentin Andrei ,  Nicolas Galoppo Von Borries ,  Varghese George ,  Yoav Harel ,  Arthur Hunter, Jr. ,  Brent Insko ,  Scott Janus ,  Pattabhiraman K ,  Mike Macpherson ,  Subramaniam Maiyuran ,  Marian Alin Petre ,  Murali Ramadoss ,  Shailesh Shah ,  Kamal Sinha ,  Prasoonkumar Surti ,  Vikranth Vemulapalli

IPC: G06F15/78 ,  G06F7/544 ,  G06F7/575 ,  G06F7/58 ,  G06F9/30 ,  G06F9/38 ,  G06F9/50 ,  G06F12/02 ,  G06F12/06 ,  G06F12/0802 ,  G06F12/0804 ,  G06F12/0811 ,  G06F12/0862 ,  G06F12/0866 ,  G06F12/0871 ,  G06F12/0875 ,  G06F12/0882 ,  G06F12/0888 ,  G06F12/0891 ,  G06F12/0893 ,  G06F12/0895 ,  G06F12/0897 ,  G06F12/1009 ,  G06F12/128 ,  G06F15/80 ,  G06F17/16 ,  G06F17/18 ,  G06T1/20 ,  G06T1/60 ,  H03M7/46 ,  G06N3/08 ,  G06T15/06

Abstract: 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.

公开(公告)号：US12141578B2

公开(公告)日：2024-11-12

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 ,  G06F7/483 ,  G06F7/544 ,  G06F9/38 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G09G5/393 ,  G06F17/16 ,  G06N20/00 ,  G06T15/00

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：US12094048B2

公开(公告)日：2024-09-17

申请号：US17497618

申请日：2021-10-08

Applicant: Intel Corporation

Inventor： Prasoonkumar Surti ,  Arthur Hunter ,  Kamal Sinha ,  Scott Janus ,  Brent Insko ,  Vasanth Ranganathan ,  Lakshminarayanan Striramassarma

IPC: G06T15/00 ,  G06T1/20 ,  G06T1/60 ,  G06T17/20

CPC classification number: G06T15/005 ,  G06T1/20 ,  G06T1/60 ,  G06T17/20

Abstract: Embodiments are generally directed to multi-tile graphics processor rendering. An embodiment of an apparatus includes a memory for storage of data; and one or more processors including a graphics processing unit (GPU) to process data, wherein the GPU includes a plurality of GPU tiles, wherein, upon geometric data being assigned to each of a plurality of screen tiles, the apparatus is to transfer the geometric data to the plurality of GPU tiles.

公开(公告)号：US12013808B2

公开(公告)日：2024-06-18

申请号：US17429873

申请日：2020-03-14

Applicant: Intel Corporation

Inventor： Altug Koker ,  Ben Ashbaugh ,  Scott Janus ,  Aravindh Anantaraman ,  Abhishek R. Appu ,  Niranjan Cooray ,  Varghese George ,  Arthur Hunter ,  Brent E. Insko ,  Elmoustapha Ould-Ahmed-Vall ,  Selvakumar Panneer ,  Vasanth Ranganathan ,  Joydeep Ray ,  Kamal Sinha ,  Lakshminarayanan Striramassarma ,  Prasoonkumar Surti ,  Saurabh Tangri

IPC: G06F13/38 ,  G06F7/544 ,  G06F7/575 ,  G06F7/58 ,  G06F9/30 ,  G06F9/38 ,  G06F9/50 ,  G06F12/02 ,  G06F12/06 ,  G06F12/0802 ,  G06F12/0804 ,  G06F12/0811 ,  G06F12/0862 ,  G06F12/0866 ,  G06F12/0871 ,  G06F12/0875 ,  G06F12/0882 ,  G06F12/0888 ,  G06F12/0891 ,  G06F12/0893 ,  G06F12/0895 ,  G06F12/0897 ,  G06F12/1009 ,  G06F12/128 ,  G06F15/78 ,  G06F15/80 ,  G06F17/16 ,  G06F17/18 ,  G06T1/20 ,  G06T1/60 ,  H03M7/46 ,  G06N3/08 ,  G06T15/06

CPC classification number: G06F15/7839 ,  G06F7/5443 ,  G06F7/575 ,  G06F7/588 ,  G06F9/3001 ,  G06F9/30014 ,  G06F9/30036 ,  G06F9/3004 ,  G06F9/30043 ,  G06F9/30047 ,  G06F9/30065 ,  G06F9/30079 ,  G06F9/3887 ,  G06F9/5011 ,  G06F9/5077 ,  G06F12/0215 ,  G06F12/0238 ,  G06F12/0246 ,  G06F12/0607 ,  G06F12/0802 ,  G06F12/0804 ,  G06F12/0811 ,  G06F12/0862 ,  G06F12/0866 ,  G06F12/0871 ,  G06F12/0875 ,  G06F12/0882 ,  G06F12/0888 ,  G06F12/0891 ,  G06F12/0893 ,  G06F12/0895 ,  G06F12/0897 ,  G06F12/1009 ,  G06F12/128 ,  G06F15/8046 ,  G06F17/16 ,  G06F17/18 ,  G06T1/20 ,  G06T1/60 ,  H03M7/46 ,  G06F9/3802 ,  G06F9/3818 ,  G06F9/3867 ,  G06F2212/1008 ,  G06F2212/1021 ,  G06F2212/1044 ,  G06F2212/302 ,  G06F2212/401 ,  G06F2212/455 ,  G06F2212/60 ,  G06N3/08 ,  G06T15/06

Abstract: Embodiments are generally directed to a multi-tile architecture for graphics operations. An embodiment of an apparatus includes a multi-tile architecture for graphics operations including a multi-tile graphics processor, the multi-tile processor includes one or more dies; multiple processor tiles installed on the one or more dies; and a structure to interconnect the processor tiles on the one or more dies, wherein the structure to enable communications between processor tiles the processor tiles.

公开(公告)号：US12007824B2

公开(公告)日：2024-06-11

申请号：US17517090

申请日：2021-11-02

Applicant: Intel Corporation

Inventor： Altug Koker ,  Abhishek R. Appu ,  Bhushan M. Borole ,  Wenyin Fu ,  Kamal Sinha ,  Joydeep Ray

IPC: G06F1/3234 ,  G06F1/3237 ,  G06F1/324 ,  G06F1/3296 ,  G09G5/36

CPC classification number: G06F1/3234 ,  G06F1/3237 ,  G06F1/324 ,  G06F1/3296 ,  G09G5/363 ,  G09G5/366 ,  G09G2310/066 ,  G09G2310/08 ,  G09G2340/02 ,  G09G2360/06 ,  G09G2360/08 ,  G09G2370/022 ,  G09G2370/16

Abstract: In one embodiment, a processor includes: a graphics processor to execute a workload; and a power controller coupled to the graphics processor. The power controller may include a voltage ramp circuit to receive a request for the graphics processor to operate at a first performance state having a first operating voltage and a first operating frequency and cause an output voltage of a voltage regulator to increase to the first operating voltage. The voltage ramp circuit may be configured to enable the graphics processor to execute the workload at an interim performance state having an interim operating voltage and an interim operating frequency when the output voltage reaches a minimum operating voltage. Other embodiments are described and claimed.

公开(公告)号：US11631198B2

公开(公告)日：2023-04-18

申请号：US17355271

申请日：2021-06-23

Applicant: Intel Corporation

Inventor： Abhishek Appu ,  Altug Koker ,  Joydeep Ray ,  Balaji Vembu ,  Prasoonkumar Surti ,  Kamal Sinha ,  Nadathur Rajagopalan Satish ,  Narayan Srinivasa ,  Feng Chen ,  Dukhwan Kim ,  Farshad Akhbari

IPC: G06T9/00 ,  G06T1/60 ,  G06N3/063 ,  G06N3/04 ,  G06N3/084 ,  G06F12/02 ,  G06T1/20

Abstract: An apparatus to facilitate compute compression is disclosed. The apparatus includes a graphics processing unit including mapping logic to map a first block of integer pixel data to a compression block and compression logic to compress the compression block.

公开(公告)号：US11609856B2

公开(公告)日：2023-03-21

申请号：US17206514

申请日：2021-03-19

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: G06F12/08 ,  G06F12/0837 ,  G06N3/08 ,  G06N20/00 ,  G06T1/20 ,  G06F12/0815 ,  G06N3/063 ,  G06N3/04 ,  G06N3/084 ,  G06N3/088

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.