公开(公告)号：US20230195685A1

公开(公告)日：2023-06-22

申请号：US18170900

申请日：2023-02-17

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Shubra Marwaha ,  Ashutosh Garg ,  Supratim Pal ,  Jorge Parra ,  Chandra Gurram ,  Varghese George ,  Darin Starkey ,  Guei-Yuan Lueh

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

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

Abstract: Described herein is a graphics processing unit (GPU) configured to receive an instruction having multiple operands, where the instruction is a single instruction multiple data (SIMD) instruction configured to use a bfloat16 (BF16) number format and the BF16 number format is a sixteen-bit floating point format having an eight-bit exponent. The GPU can process the instruction using the multiple operands, where to process the instruction includes to perform a multiply operation, perform an addition to a result of the multiply operation, and apply a rectified linear unit function to a result of the addition.

公开(公告)号：US11640297B2

公开(公告)日：2023-05-02

申请号：US17304153

申请日：2021-06-15

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Guei-Yuan Lueh ,  Supratim Pal ,  Ashutosh Garg ,  Chandra S. Gurram ,  Jorge E. Parra ,  Junjie Gu ,  Konrad Trifunovic ,  Hong Bin Liao ,  Mike B. MacPherson ,  Shubh B. Shah ,  Shubra Marwaha ,  Stephen Junkins ,  Timothy R. Bauer ,  Varghese George ,  Weiyu Chen

IPC: G06F9/30 ,  G06T1/20 ,  G06F9/38

Abstract: Embodiments described herein provided for an instruction and associated logic to enable GPGPU program code to access special purpose hardware logic to accelerate dot product operations. One embodiment provides for a graphics processing unit comprising a fetch unit to fetch an instruction for execution and a decode unit to decode the instruction into a decoded instruction. The decoded instruction is a matrix instruction to cause the graphics processing unit to perform a parallel dot product operation. The GPGPU also includes systolic dot product circuitry to execute the decoded instruction across one or more SIMD lanes using multiple systolic layers, wherein to execute the decoded instruction, a dot product computed at a first systolic layer is to be output to a second systolic layer, wherein each systolic layer includes one or more sets of interconnected multipliers and adders, each set of multipliers and adders to generate a dot product.

公开(公告)号：US11042370B2

公开(公告)日：2021-06-22

申请号：US15957728

申请日：2018-04-19

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Guei-Yuan Lueh ,  Supratim Pal ,  Ashutosh Garg ,  Chandra S. Gurram ,  Jorge E. Parra ,  Junjie Gu ,  Konrad Trifunovic ,  Hong Bin Liao ,  Mike B. Macpherson ,  Shubh B. Shah ,  Shubra Marwaha ,  Stephen Junkins ,  Timothy R. Bauer ,  Varghese George ,  Weiyu Chen

IPC: G06F9/30 ,  G06T1/20 ,  G06F9/38

Abstract: Embodiments described herein provided for an instruction and associated logic to enable GPGPU program code to access special purpose hardware logic to accelerate dot product operations. One embodiment provides for a graphics processing unit comprising a fetch unit to fetch an instruction for execution and a decode unit to decode the instruction into a decoded instruction. The decoded instruction is a matrix instruction to cause the graphics processing unit to perform a parallel dot product operation. The GPGPU also includes a systolic dot product unit to execute the decoded instruction across one or more SIMD lanes using multiple systolic layers, wherein to execute the decoded instruction, a dot product computed at a first systolic layer is to be output to a second systolic layer, wherein each systolic layer includes one or more sets of interconnected multipliers and adders, each set of multipliers and adders to generate a dot product.

公开(公告)号：US20210081201A1

公开(公告)日：2021-03-18

申请号：US17107823

申请日：2020-11-30

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Jorge Parra ,  Ashutosh Garg ,  Chandra Gurram ,  Chunhui Mei ,  Durgesh Borkar ,  Shubra Marwaha ,  Supratim Pal ,  Varghese George ,  Wei Xiong ,  Yan Li ,  Yongsheng Liu ,  Dipankar Das ,  Sasikanth Avancha ,  Dharma Teja Vooturi ,  Naveen K. Mellempudi

IPC: G06F9/30 ,  G06F9/38 ,  G06F15/80

Abstract: An apparatus to facilitate utilizing structured sparsity in systolic arrays is disclosed. The apparatus includes a processor comprising a systolic array to receive data from a plurality of source registers, the data comprising unpacked source data, structured source data that is packed based on sparsity, and metadata corresponding to the structured source data; identify portions of the unpacked source data to multiply with the structured source data, the portions of the unpacked source data identified based on the metadata; and output, to a destination register, a result of multiplication of the portions of the unpacked source data and the structured source data.

公开(公告)号：US20240362180A1

公开(公告)日：2024-10-31

申请号：US18647549

申请日：2024-04-26

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Shubra Marwaha ,  Ashutosh Garg ,  Supratim Pal ,  Jorge Parra ,  Chandra Gurram ,  Varghese George ,  Darin Starkey ,  Guei-Yuan Lueh

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 ,  G06N3/08 ,  G06T1/20 ,  G06T1/60 ,  G06T15/06 ,  H03M7/46

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: Graphics processors and graphics processing units having dot product accumulate instructions for a hybrid floating point format are disclosed. In one embodiment, a graphics multiprocessor comprises an instruction unit to dispatch instructions and a processing resource coupled to the instruction unit. The processing resource is configured to receive a dot product accumulate instruction from the instruction unit and to process the dot product accumulate instruction using a bfloat16 number (BF16) format.

公开(公告)号：US20220365901A1

公开(公告)日：2022-11-17

申请号：US17827067

申请日：2022-05-27

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Shubra Marwaha ,  Ashutosh Garg ,  Supratim Pal ,  Jorge Parra ,  Chandra Gurram ,  Varghese George ,  Darin Starkey ,  Guei-Yuan Lueh

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

Abstract: Described herein is a graphics processing unit (GPU) comprising a first processing cluster to perform parallel processing operations, the parallel processing operations including a ray tracing operation and a matrix multiply operation; and a second processing cluster coupled to the first processing cluster, wherein the first processing cluster includes a floating-point unit to perform floating point operations, the floating-point unit is configured to process an instruction using a bfloat16 (BF16) format with a multiplier to multiply second and third source operands while an accumulator adds a first source operand with output from the multiplier.

公开(公告)号：US20220129266A1

公开(公告)日：2022-04-28

申请号：US17428523

申请日：2020-03-14

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Shubra Marwaha ,  Ashutosh Garg ,  Supratim Pal ,  Jorge Parra ,  Chandra Gurram ,  Varghese George ,  Darin Starkey ,  Guei-Yuan Lueh

IPC: G06F9/30 ,  G06F7/544 ,  G06F12/02 ,  G06F12/0811 ,  G06F12/0875

Abstract: Graphics processors and graphics processing units having dot product accumulate instructions for a hybrid floating point format are disclosed. In one embodiment, a graphics multiprocessor comprises an instruction unit to dispatch instructions and
a processing resource coupled to the instruction unit. The processing resource is configured to receive a dot product accumulate instruction from the instruction unit and to process the dot product accumulate instruction using a bfloat16 number (BF16) format.

公开(公告)号：US20210312697A1

公开(公告)日：2021-10-07

申请号：US17304092

申请日：2021-06-14

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Shubra Marwaha ,  Ashutosh Garg ,  Supratim Pal ,  Jorge Parra ,  Chandra Gurram ,  Varghese George ,  Darin Starkey ,  Guei-Yuan Lueh

IPC: G06T15/06 ,  G06F9/38 ,  G06F17/18 ,  G06F9/30

Abstract: Described herein is a graphics processing unit (GPU) comprising a single instruction, multiple thread (SIMT) multiprocessor comprising an instruction cache, a shared memory coupled with the instruction cache, and circuitry coupled with the shared memory and the instruction cache, the circuitry including multiple texture units, a first core including hardware to accelerate matrix operations, and a second core configured to receive an instruction having multiple operands in a bfloat16 (BF16) number format, wherein the multiple operands include a first source operand, a second source operand, and a third source operand, and the BF16 number format is a sixteen-bit floating point format having an eight-bit exponent and process the instruction, wherein to process the instruction includes to multiply the second source operand by the third source operand and add a first source operand to a result of the multiply.

公开(公告)号：US20240320000A1

公开(公告)日：2024-09-26

申请号：US18621539

申请日：2024-03-29

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Jorge Parra ,  Ashutosh Garg ,  Chandra Gurram ,  Chunhui Mei ,  Durgesh Borkar ,  Shubra Marwaha ,  Supratim Pal ,  Varghese George ,  Wei Xiong ,  Yan Li ,  Yongsheng Liu ,  Dipankar Das ,  Sasikanth Avancha ,  Dharma Teja Vooturi ,  Naveen K. Mellempudi

IPC: G06F9/30 ,  G06F9/38 ,  G06F15/80

CPC classification number: G06F9/30036 ,  G06F9/3001 ,  G06F9/30101 ,  G06F9/3893 ,  G06F15/8046

Abstract: An apparatus to facilitate utilizing structured sparsity in systolic arrays is disclosed. The apparatus includes a processor comprising a systolic array to receive data from a plurality of source registers, the data comprising unpacked source data, structured source data that is packed based on sparsity, and metadata corresponding to the structured source data; identify portions of the unpacked source data to multiply with the structured source data, the portions of the unpacked source data identified based on the metadata; and output, to a destination register, a result of multiplication of the portions of the unpacked source data and the structured source data.

公开(公告)号：US11709793B2

公开(公告)日：2023-07-25

申请号：US17827067

申请日：2022-05-27

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Shubra Marwaha ,  Ashutosh Garg ,  Supratim Pal ,  Jorge Parra ,  Chandra Gurram ,  Varghese George ,  Darin Starkey ,  Guei-Yuan Lueh

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

CPC classification number: G06F15/7839 ,  G06F7/5443 ,  G06F7/575 ,  G06F7/588 ,  G06F9/3001 ,  G06F9/3004 ,  G06F9/30014 ,  G06F9/30036 ,  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: Described herein is a graphics processing unit (GPU) comprising a first processing cluster to perform parallel processing operations, the parallel processing operations including a ray tracing operation and a matrix multiply operation; and a second processing cluster coupled to the first processing cluster, wherein the first processing cluster includes a floating-point unit to perform floating point operations, the floating-point unit is configured to process an instruction using a bfloat16 (BF16) format with a multiplier to multiply second and third source operands while an accumulator adds a first source operand with output from the multiplier.