公开(公告)号：US11809978B2

公开(公告)日：2023-11-07

申请号：US17723074

申请日：2022-04-18

Applicant: Intel Corporation

Inventor： Liwei Ma ,  Nadathur Rajagopalan Satish ,  Jeremy Bottleson ,  Farshad Akhbari ,  Eriko Nurvitadhi ,  Chandrasekaran Sakthivel ,  Barath Lakshmanan ,  Jingyi Jin ,  Justin E. Gottschlich ,  Michael Strickland

IPC: G06N3/04 ,  G06N3/044 ,  G06F9/50 ,  G06N3/063 ,  G06N3/084 ,  G06N3/045

CPC classification number: G06N3/044 ,  G06F9/5038 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084 ,  G06F2209/5021

Abstract: An apparatus to facilitate workload scheduling is disclosed. The apparatus includes one or more clients, one or more processing units to processes workloads received from the one or more clients, including hardware resources and scheduling logic to schedule direct access of the hardware resources to the one or more clients to process the workloads.

公开(公告)号：US20230061670A1

公开(公告)日：2023-03-02

申请号：US17978573

申请日：2022-11-01

Applicant: Intel Corporation

Inventor： Elmoustapha Ould-Ahmed-Vall ,  Sara S. Baghsorkhi ,  Anbang Yao ,  Kevin Nealis ,  Xiaoming Chen ,  Altug Koker ,  Abhishek R. Appu ,  John C. Weast ,  Mike B. Macpherson ,  Dukhwan Kim ,  Linda L. Hurd ,  Ben J. Ashbaugh ,  Barath Lakshmanan ,  Liwei Ma ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland

IPC: G06T1/20 ,  G06N3/08 ,  G06F9/38 ,  G06N3/063 ,  G06F9/30 ,  G06N20/00 ,  G06N3/04 ,  G06F9/50 ,  G06F7/483

Abstract: One embodiment provides an apparatus comprising a memory stack including multiple memory dies and a parallel processor including a plurality of multiprocessors. Each multiprocessor has a single instruction, multiple thread (SIMT) architecture, the parallel processor coupled to the memory stack via one or more memory interfaces. At least one multiprocessor comprises a multiply-accumulate circuit to perform multiply-accumulate operations on matrix data in a stage of a neural network implementation to produce a result matrix comprising a plurality of matrix data elements at a first precision, precision tracking logic to evaluate metrics associated with the matrix data elements and indicate if an optimization is to be performed for representing data at a second stage of the neural network implementation, and a numerical transform unit to dynamically perform a numerical transform operation on the matrix data elements based on the indication to produce transformed matrix data elements at a second precision.

公开(公告)号：US20230027203A1

公开(公告)日：2023-01-26

申请号：US17826674

申请日：2022-05-27

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 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08

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：US11468541B2

公开(公告)日：2022-10-11

申请号：US17720804

申请日：2022-04-14

Applicant: Intel Corporation

Inventor： Elmoustapha Ould-Ahmed-Vall ,  Sara S. Baghsorkhi ,  Anhang Yao ,  Kevin Nealis ,  Xiaoming Chen ,  Altug Koker ,  Abhishek R. Appu ,  John C. Weast ,  Mike B. Macpherson ,  Dukhwan Kim ,  Linda L. Hurd ,  Ben J. Ashbaugh ,  Barath Lakshmanan ,  Liwei Ma ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland

IPC: G06T1/20 ,  G06F7/483 ,  G06N20/00 ,  G06F3/14 ,  G06T1/60 ,  G06N3/08 ,  G06F9/30 ,  G06N3/04 ,  G06N3/063 ,  G06F9/50 ,  G06F9/38 ,  G06T15/00

Abstract: Embodiments described herein provide a graphics processor that can perform a variety of mixed and multiple precision instructions and operations. One embodiment provides a streaming multiprocessor that can concurrently execute multiple thread groups, wherein the streaming multiprocessor includes a single instruction, multiple thread (SIMT) architecture and the streaming multiprocessor is to execute multiple threads for each of multiple instructions. The streaming multiprocessor can perform concurrent integer and floating-point operations and includes a mixed precision core to perform operations at multiple or mixed precisions and dynamic ranges.

公开(公告)号：US11461107B2

公开(公告)日：2022-10-04

申请号：US16227645

申请日：2018-12-20

Applicant: Intel Corporation

Inventor： Elmoustapha Ould-Ahmed-Vall ,  Barath Lakshmanan ,  Tatiana Shpeisman ,  Joydeep Ray ,  Ping T. Tang ,  Michael Strickland ,  Xiaoming Chen ,  Anbang Yao ,  Ben J. Ashbaugh ,  Linda L. Hurd ,  Liwei Ma

IPC: G06F9/38 ,  G06F9/30 ,  G06F15/80 ,  G06F13/42 ,  G06F13/40 ,  G06N20/00 ,  G06T1/20 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06N20/10 ,  G06F9/50 ,  G06N3/00

Abstract: One embodiment provides for a general-purpose graphics processing unit comprising a streaming multiprocessor having a single instruction, multiple thread (SIMT) architecture including hardware multithreading. The streaming multiprocessor comprises multiple processing blocks including multiple processing cores. The processing cores include independent integer and floating-point data paths that are configurable to concurrently execute multiple independent instructions. A memory is coupled with the multiple processing blocks.

公开(公告)号：US20220253317A1

公开(公告)日：2022-08-11

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 ,  G06T1/20 ,  G06F9/448

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：US11409537B2

公开(公告)日：2022-08-09

申请号：US15819167

申请日：2017-11-21

Applicant: Intel Corporation

Inventor： Elmoustapha Ould-Ahmed-Vall ,  Barath Lakshmanan ,  Tatiana Shpeisman ,  Joydeep Ray ,  Ping T. Tang ,  Michael Strickland ,  Xiaoming Chen ,  Anbang Yao ,  Ben J. Ashbaugh ,  Linda L. Hurd ,  Liwei Ma

IPC: G06F9/38 ,  G06F9/30 ,  G06F13/40 ,  G06F13/42 ,  G06N20/00 ,  G06T1/20 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06N20/10 ,  G06F9/50 ,  G06F15/80 ,  G06N3/00

Abstract: One embodiment provides for a graphics processing unit (GPU) to accelerate machine learning operations, the GPU comprising an instruction cache to store a first instruction and a second instruction, the first instruction to cause the GPU to perform a floating-point operation, including a multi-dimensional floating-point operation, and the second instruction to cause the GPU to perform an integer operation; and a general-purpose graphics compute unit having a single instruction, multiple thread (SIMT) architecture, the general-purpose graphics compute unit to simultaneously execute the first instruction and the second instruction, wherein the integer operation corresponds to a memory address calculation.

公开(公告)号：US11269643B2

公开(公告)日：2022-03-08

申请号：US15482798

申请日：2017-04-09

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 ,  G06T1/20 ,  G06F9/448

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：US11055613B2

公开(公告)日：2021-07-06

申请号：US16463764

申请日：2016-12-28

Applicant: Intel Corporation

Inventor： Liwei Ma

IPC: G06N3/063 ,  G06F7/544 ,  H03K19/21 ,  H03K19/23

Abstract: In one embodiment, an apparatus comprises a plurality of bitwise multipliers, a bitwise multiplier of the plurality of bitwise multipliers to multiply a binary synapse weight value of a neural network by a binary activation state value of a neuron of the neural network. The apparatus further comprises a plurality of majority voters, a majority voter of the plurality of majority voters to receive outputs of a first group of bitwise multipliers and to generate a majority result to indicate whether a majority of outputs of the first group of bitwise multipliers are set to a first binary value or a second binary value. The apparatus also comprises a first plurality of reconfigurable connections coupled to outputs of the plurality of bitwise multipliers and inputs of the plurality of majority voters.

公开(公告)号：US11048970B2

公开(公告)日：2021-06-29

申请号：US16304676

申请日：2016-06-03

Applicant: Intel Corporation

Inventor： Liwei Ma ,  Jiqiang Song

IPC: G06K9/62 ,  G06T7/73 ,  G06K9/00 ,  G06K9/46 ,  G06N3/04

Abstract: Embodiments provide for a processor including logic to accelerate convolutional neural network processing, the processor including first logic to apply a convolutional layer to an image to generate a first convolution result and second logic to apply a look-up convolutional layer to the first convolution result to generate a second convolution result, the second convolution result associated with a location of the first convolution result within a global filter kernel.