公开(公告)号：US20180307494A1

公开(公告)日：2018-10-25

申请号：US15494773

申请日：2017-04-24

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 ,  G06F13/42 ,  G06F13/40 ,  G06N99/00

CPC classification number: G06F9/3887 ,  G06F1/32 ,  G06F9/3001 ,  G06F9/30014 ,  G06F9/30036 ,  G06F9/30094 ,  G06F9/30109 ,  G06F9/30112 ,  G06F9/3016 ,  G06F9/3851 ,  G06F9/3891 ,  G06F9/50 ,  G06F13/4068 ,  G06F13/4282 ,  G06F15/80 ,  G06F2213/0026 ,  G06N3/00 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/084 ,  G06N20/00 ,  G06T1/20

Abstract: One embodiment provides for a compute apparatus to perform machine learning operations, the compute apparatus comprising instruction decode logic to decode a single instruction including multiple operands into a single decoded instruction, the multiple operands having differing precisions and a general-purpose graphics compute unit including a first logic unit and a second logic unit, the general-purpose graphics compute unit to execute the single decoded instruction, wherein to execute the single decoded instruction includes to perform a first instruction operation on a first set of operands of the multiple operands at a first precision and a simultaneously perform second instruction operation on a second set of operands of the multiple operands at a second precision.

公开(公告)号：US09324303B2

公开(公告)日：2016-04-26

申请号：US13993664

申请日：2012-12-27

Applicant: Intel Corporation

Inventor： Liwei Ma ,  Jiqiang Song ,  Gansha Wu ,  Zhuo Wang

IPC: G06F3/033 ,  G09G5/37 ,  G06F3/0346 ,  G06F3/0354 ,  G06F3/14 ,  G06F1/16

CPC classification number: G09G5/37 ,  G06F1/1616 ,  G06F1/1677 ,  G06F3/0346 ,  G06F3/0354 ,  G06F3/1446 ,  G06F2200/1637

Abstract: Embodiments of techniques and apparatus for open angle detection and processing are described. In embodiments, an apparatus may comprise a first panel having a display, a second panel movably coupled with the first panel. The first and second panels variably define an angle between these two panels. One or more sensors may be disposed in the apparatus and configured to detect an angle change event of the variable angle between two panels. The angle changing information may be used by an application to vary output of the application onto the display. Other embodiments may be described and claimed.

Abstract translation: 描述了用于开放角度检测和处理的技术和装置的实施例。 在实施例中，装置可以包括具有显示器的第一面板，与第一面板可移动地联接的第二面板。 第一和第二面板可变地限定这两个面板之间的角度。 一个或多个传感器可以设置在设备中并且被配置为检测两个面板之间的可变角度的角度变化事件。 角度变化信息可以由应用使用以将应用的输出改变到显示器上。 可以描述和要求保护其他实施例。

公开(公告)号：US12148063B2

公开(公告)日：2024-11-19

申请号：US17960611

申请日：2022-10-05

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 ,  G06F7/483 ,  G06F9/30 ,  G06F9/38 ,  G06F9/50 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084 ,  G06N20/00 ,  G06T1/60 ,  G06F3/14 ,  G06T15/00

Abstract: One embodiment provides a multi-chip module accelerator usable to execute tensor data processing operations a multi-chip module. The multi-chip module may include a memory stack including multiple memory dies and parallel processor circuitry communicatively coupled to the memory stack. The parallel processor circuitry may include multiprocessor cores to execute matrix multiplication and accumulate operations. The matrix multiplication and accumulate operations may include floating-point operations that are configurable to include two-dimensional matrix multiply and accumulate operations involving inputs that have differing floating-point precisions. The floating-point operations may include a first operation at a first precision and a second operation at a second precision. The first operation may include a multiply having at least one 16-bit floating-point input and the second operation may include an accumulate having a 32-bit floating-point input.

公开(公告)号：US20240256825A1

公开(公告)日：2024-08-01

申请号：US18435528

申请日：2024-02-07

Applicant: Intel Corporation

Inventor： Liwei Ma ,  Elmoustapha Ould-Ahmed-Vall ,  Barath Lakshmanan ,  Ben J. Ashbaugh ,  Jingyi Jin ,  Jeremy Bottleson ,  Mike B. Macpherson ,  Kevin Nealis ,  Dhawal Srivastava ,  Joydeep Ray ,  Ping T. Tang ,  Michael S. Strickland ,  Xiaoming Chen ,  Anbang Yao ,  Tatiana Shpeisman ,  Altug Koker ,  Abhishek R. Appu

IPC: G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/082 ,  G06T1/20

CPC classification number: G06N3/04 ,  G06N3/063 ,  G06N3/082 ,  G06T1/20 ,  G06N3/044 ,  G06N3/045

Abstract: A library of machine learning primitives is provided to optimize a machine learning model to improve the efficiency of inference operations. In one embodiment a trained convolutional neural network (CNN) model is processed into a trained CNN model via pruning, convolution window optimization, and quantization.

公开(公告)号：US11810405B2

公开(公告)日：2023-11-07

申请号：US17539083

申请日：2021-11-30

Applicant: Intel Corporation

Inventor： Barath Lakshamanan ,  Linda L. Hurd ,  Ben J. Ashbaugh ,  Elmoustapha Ould-Ahmed-Vall ,  Liwei Ma ,  Jingyi Jin ,  Justin E. Gottschlich ,  Chandrasekaran Sakthivel ,  Michael S. Strickland ,  Brian T. Lewis ,  Lindsey Kuper ,  Altug Koker ,  Abhishek R. Appu ,  Prasoonkumar Surti ,  Joydeep Ray ,  Balaji Vembu ,  Javier S. Turek ,  Naila Farooqui

IPC: G01C22/00 ,  G07C5/00 ,  G05D1/00 ,  G08G1/01 ,  H04L67/12 ,  G06N20/00 ,  G06F9/50 ,  G01C21/34 ,  B60W30/00 ,  G06N3/063 ,  G06N3/084 ,  G06N20/10 ,  G06N3/044 ,  G06N3/045 ,  G08G1/052 ,  G01S19/13 ,  H04L43/0852 ,  G05D1/02 ,  H04L43/16

CPC classification number: G07C5/008 ,  B60W30/00 ,  G01C21/34 ,  G01C21/3415 ,  G01C21/3492 ,  G05D1/0088 ,  G06F9/5027 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084 ,  G06N20/00 ,  G06N20/10 ,  G08G1/012 ,  H04L67/12 ,  G01S19/13 ,  G05D1/0257 ,  G05D2201/0213 ,  G06F2209/509 ,  G08G1/0112 ,  G08G1/052 ,  H04L43/0852 ,  H04L43/16

Abstract: An autonomous vehicle is provided that includes one or more processors configured to provide a local compute manager to manage execution of compute workloads associated with the autonomous vehicle. The local compute manager can perform various compute operations, including receiving offload of compute operations from to other compute nodes and offloading compute operations to other compute notes, where the other compute nodes can be other autonomous vehicles. The local compute manager can also facilitate autonomous navigation functionality.

公开(公告)号：US11748841B2

公开(公告)日：2023-09-05

申请号：US17871781

申请日：2022-07-22

Applicant: Intel Corporation

Inventor： Abhishek R. Appu ,  Altug Koker ,  John C. Weast ,  Mike B. Macpherson ,  Linda L. Hurd ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Prasoonkumar Surti ,  Chandrasekaran Sakthivel ,  Liwei Ma ,  Elmoustapha Ould-Ahmed-Vall ,  Kamal Sinha ,  Joydeep Ray ,  Balaji Vembu ,  Sanjeev Jahagirdar ,  Vasanth Ranganathan ,  Dukhwan Kim

IPC: G06T1/20 ,  G06N3/063 ,  G06F9/46 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084 ,  G06N3/044

CPC classification number: G06T1/20 ,  G06F9/46 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N3/044 ,  G06N3/084

Abstract: A mechanism is described for facilitating inference coordination and processing utilization for machine learning. A method of embodiments, as described herein, includes limiting execution of workloads for the respective contexts of a plurality of contexts to a specified subset of a plurality of processing resources of a processing system according to physical resource slices of the processing system that are associated with the respective contexts of the plurality of contexts.

公开(公告)号：US11430082B2

公开(公告)日：2022-08-30

申请号：US17143805

申请日：2021-01-07

Applicant: Intel Corporation

Inventor： Abhishek R. Appu ,  Altug Koker ,  John C. Weast ,  Mike B. Macpherson ,  Linda L. Hurd ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Prasoonkumar Surti ,  Chandrasekaran Sakthivel ,  Liwei Ma ,  Elmoustapha Ould-Ahmed-Vall ,  Kamal Sinha ,  Joydeep Ray ,  Balaji Vembu ,  Sanjeev Jahagirdar ,  Vasanth Ranganathan ,  Dukhwan Kim

IPC: G06T1/20 ,  G06F9/46 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08

Abstract: A mechanism is described for facilitating inference coordination and processing utilization for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting, at training time, information relating to one or more tasks to be performed according to a training dataset relating to a processor including a graphics processor. The method may further include analyzing the information to determine one or more portions of hardware relating to the processor capable of supporting the one or more tasks, and configuring the hardware to pre-select the one or more portions to perform the one or more tasks, while other portions of the hardware remain available for other tasks.

公开(公告)号：US20220245753A1

公开(公告)日：2022-08-04

申请号：US17720804

申请日：2022-04-14

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 ,  G06F7/483 ,  G06N3/08 ,  G06F9/30 ,  G06N3/04 ,  G06N3/063 ,  G06F9/50 ,  G06F9/38 ,  G06N20/00

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：US11315007B2

公开(公告)日：2022-04-26

申请号：US16918220

申请日：2020-07-01

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 Strikland

IPC: G06N3/04 ,  G06F9/50 ,  G06N3/063 ,  G06N3/08

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：US11308574B2

公开(公告)日：2022-04-19

申请号：US16983080

申请日：2020-08-03

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 ,  G06F7/483 ,  G06N3/08 ,  G06F9/30 ,  G06N3/04 ,  G06N3/063 ,  G06F9/50 ,  G06F9/38 ,  G06N20/00 ,  G06F3/14 ,  G06T1/60 ,  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 precisions.