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.

公开(公告)号：US11361496B2

公开(公告)日：2022-06-14

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 ,  G06F9/30 ,  G06F9/38 ,  G06F17/18

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：US11188618B2

公开(公告)日：2021-11-30

申请号：US16561715

申请日：2019-09-05

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Mathew Nevin ,  Jorge Parra ,  Ashutosh Garg ,  Shubra Marwaha ,  Shubh Shah

IPC: G06F17/16 ,  G06F7/487 ,  G06F9/30 ,  G06F13/16

Abstract: An apparatus to facilitate acceleration of matrix multiplication operations. The apparatus comprises a systolic array including matrix multiplication hardware to perform multiply-add operations on received matrix data comprising data from a plurality of input matrices and sparse matrix acceleration hardware to detect zero values in the matrix data and perform one or more optimizations on the matrix data to reduce multiply-add operations to be performed by the matrix multiplication hardware.

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：US12039001B2

公开(公告)日：2024-07-16

申请号：US18301386

申请日：2023-04-17

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Jorge Parra ,  Supratim Pal ,  Ashutosh Garg ,  Shubra Marwaha ,  Chandra Gurram ,  Darin Starkey ,  Durgesh Borkar ,  Varghese George

IPC: G06F17/16 ,  G06F9/30 ,  G06F15/80

CPC classification number: G06F17/16 ,  G06F9/3001 ,  G06F9/30145 ,  G06F15/8046

Abstract: Described herein is a graphics processor including a plurality of processing clusters coupled with a host interface, each processing cluster comprising a plurality of multiprocessors, the plurality of multiprocessors interconnected via a data interconnect, and each multiprocessor comprising sparse matrix multiply acceleration hardware including a systolic processing array with feedback inputs.

公开(公告)号：US12008067B2

公开(公告)日：2024-06-11

申请号：US17527324

申请日：2021-11-16

Applicant: Intel Corporation

Inventor： Subramaniam Maiyuran ,  Mathew Nevin ,  Jorge Parra ,  Ashutosh Garg ,  Shubra Marwaha ,  Shubh Shah

IPC: G06F17/16 ,  G06F7/487 ,  G06F9/30 ,  G06F13/16

CPC classification number: G06F17/16 ,  G06F7/4876 ,  G06F9/3001 ,  G06F9/30036 ,  G06F13/1673 ,  G06F2207/3892

Abstract: An apparatus to facilitate acceleration of matrix multiplication operations. The apparatus comprises a systolic array including matrix multiplication hardware to perform multiply-add operations on received matrix data comprising data from a plurality of input matrices and sparse matrix acceleration hardware to detect zero values in the matrix data and perform one or more optimizations on the matrix data to reduce multiply-add operations to be performed by the matrix multiplication hardware.