公开(公告)号：US20160182405A1

公开(公告)日：2016-06-23

申请号：US14579729

申请日：2014-12-22

Applicant: Intel Corporation

Inventor： Gregory K. Chen ,  Mark A. Anders ,  Himanshu Kaul ,  Ram K. Krishnamurthy ,  Yejoong Kim

IPC: H04L12/933 ,  H04L12/911

CPC classification number: H04L49/109 ,  H04L45/60 ,  H04L49/60

Abstract: A packet-switched reservation request to be associated with a first data stream is received. A communication mode is selected. The communication mode is to be either a circuit-switched mode or a packet-switched mode. At least a portion of the first data stream is communicated in accordance with the communication mode.

Abstract translation: 接收与第一数据流相关联的分组交换预约请求。 选择通信模式。 通信模式是电路交换模式或分组交换模式。 根据通信模式来传送第一数据流的至少一部分。

公开(公告)号：US20160182245A1

公开(公告)日：2016-06-23

申请号：US14574294

申请日：2014-12-17

Applicant: Intel Corporation

Inventor： Gregory K. Chen ,  Mark A. Anders ,  Himanshu Kaul

IPC: H04L12/18 ,  H04L12/935 ,  H04L12/933

CPC classification number: H04L12/1886 ,  H04L45/16 ,  H04L49/109 ,  H04L49/3009

Abstract: A multicast message that is to originate from a source is received. The multicast message comprises an identifier. A plurality of directions in which the multicast message is to fork at the router are stored. A plurality of messages from the directions in which the multicast message is to fork are received. The received messages are to comprise the identifier. The plurality of messages are aggregated into an aggregate message and sent towards the source.

Abstract translation: 将收到源自源的多播消息。 多播消息包括标识符。 存储多路传输消息在路由器上分叉的多个方向。 接收从多播消息到叉的方向的多个消息。 所接收的消息将包括标识符。 多个消息被聚合成聚合消息并被发送给源。

公开(公告)号：US20160179670A1

公开(公告)日：2016-06-23

申请号：US14573968

申请日：2014-12-17

Applicant: Intel Corporation

Inventor： Mark A. Anders ,  Himanshu Kaul ,  Gregory K. Chen

IPC: G06F12/08

CPC classification number: G06F12/0806 ,  G06F12/0824 ,  G06F12/0893 ,  G06F2212/1021 ,  G06F2212/284 ,  G06F2212/608 ,  Y02D10/13

Abstract: A first pointer dereferencer receives a location of a portion of a first node of a data structure. The first node is to be stored in a first storage element. A first pointer is obtained from the first node of the data structure. A location of a portion of a second node of the data structure is determined based on the first pointer. The second node is to be stored in a second storage element. The location of the portion of the second node of the data structure is sent to a second pointer dereferencer that is to access the portion of the second node from the second storage element.

Abstract translation: 第一指针解引用器接收数据结构的第一节点的一部分的位置。 第一个节点要存储在第一个存储元件中。 从数据结构的第一个节点获取第一个指针。 基于第一指针确定数据结构的第二节点的一部分的位置。 第二节点将被存储在第二存储元件中。 数据结构的第二节点的部分的位置被发送到第二指针解引用器，其将从第二存储元件访问第二节点的该部分。

公开(公告)号：US12039331B2

公开(公告)日：2024-07-16

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

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 ,  G06F1/16 ,  G06F9/30025 ,  G06F9/3013 ,  G06F2207/3824 ,  G06N20/00 ,  G06T15/005

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.

公开(公告)号：US20240232115A1

公开(公告)日：2024-07-11

申请号：US18528509

申请日：2023-12-04

Applicant: Intel Corporation

Inventor： Himanshu Kaul ,  Mark A. Anders ,  Gregory K. Chen

IPC: G06F13/40 ,  H04L12/54 ,  H04L49/10

CPC classification number: G06F13/4022 ,  H04L12/54 ,  H04L49/10

Abstract: An apparatus includes a first port set that includes an input port and an output port. The apparatus further includes a plurality of second port sets. Each of the second port sets includes an input port coupled to the output port of the first port set and an output port coupled to the input port of the first port set. The plurality of second port sets are to each communicate at a first maximum bandwidth and the first port set is to communicate at a second maximum bandwidth that is higher than the first maximum bandwidth.

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：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.

公开(公告)号：US11169799B2

公开(公告)日：2021-11-09

申请号：US16432402

申请日：2019-06-05

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 ,  G06F9/38 ,  G06F7/483 ,  G06F7/544 ,  G06N3/063 ,  G06N20/00 ,  G09G5/393 ,  G06N3/04 ,  G06N3/08 ,  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：US20210182058A1

公开(公告)日：2021-06-17

申请号：US17169232

申请日：2021-02-05

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 ,  G06N3/04 ,  G06F9/38 ,  G06F7/483 ,  G09G5/393 ,  G06F7/544 ,  G06N3/063 ,  G06N3/08

Abstract: A processing apparatus is provided comprising a multiprocessor having a multithreaded architecture. The multiprocessor can execute at least one single instruction to perform parallel mixed precision matrix operations. In one embodiment the apparatus includes a memory interface and an array of multiprocessors coupled to the memory interface. At least one multiprocessor in the array of multiprocessors is configured to execute a fused multiply-add instruction in parallel across multiple threads.

公开(公告)号：US10303735B2

公开(公告)日：2019-05-28

申请号：US14944828

申请日：2015-11-18

Applicant: Intel Corporation

Inventor： Himanshu Kaul ,  Mark A. Anders ,  Sanu K. Mathew

IPC: G06F17/10 ,  G06K9/00 ,  G06K9/62 ,  G06F16/903

Abstract: Systems, apparatuses, and methods for k-nearest neighbor (KNN) searches are described. In particular, embodiments of a KNN accelerator and its uses are described. In some embodiments, the KNN accelerator includes a plurality of vector partial distance computation circuits each to calculate a partial sum, a minimum sort network to sort partial sums from the plurality of vector partial distance computation circuits to find k nearest neighbor matches and a global control circuit to control aspects of operations of the plurality of vector partial distance computation circuits.