公开(公告)号：US20190050729A1

公开(公告)日：2019-02-14

申请号：US15936323

申请日：2018-03-26

Applicant: Intel Corporation

Inventor： Barath Lakshmanan ,  Palanivel Guruva Reddiar

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/063 ,  G05D1/00

Abstract: Methods and apparatus relating to deep learning solutions for safe, legal, and/or efficient autonomous driving are described. In an embodiment, first logic determines a geographic location of a vehicle, a weather condition at the geographic location, and a maneuver for the vehicle based at least in part on sensor data and a target location. Memory stores data corresponding to the geographic location, the weather condition, and the maneuver. The first logic causes one or more motion planning logic to actuate or control movement of the vehicle based on the stored data. Other embodiments are also disclosed and claimed.

公开(公告)号：US20180307983A1

公开(公告)日：2018-10-25

申请号：US15494948

申请日：2017-04-24

Applicant: Intel Corporation

Inventor： Narayan Srinivasa ,  Joydeep Ray ,  Nicolas C. Galoppo Von Borries ,  Ben Ashbaugh ,  Prasoonkumar Surti ,  Feng Chen ,  Barath Lakshmanan ,  Elmoustapha Ould-Ahmed-Vall ,  Liwei Ma ,  Linda L. Hurd ,  Abhishek R. Appu ,  John C. Weast ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Chandrasekaran Sakthivel ,  Farshad Akhbari ,  Dukhwan Kim ,  Altug Koker ,  Nadathur Rajagopalan Satish

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/082

Abstract: An apparatus to facilitate optimization of a neural network (NN) is disclosed. The apparatus includes optimization logic to define a NN topology having one or more macro layers, adjust the one or more macro layers to adapt to input and output components of the NN and train the NN based on the one or more macro layers.

公开(公告)号：US11948224B2

公开(公告)日：2024-04-02

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 ,  G06F3/14 ,  G06F7/483 ,  G06F9/30 ,  G06F9/38 ,  G06F9/50 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N3/084 ,  G06N20/00 ,  G06T1/60 ,  G06T15/00

CPC classification number: G06T1/20 ,  G06F7/483 ,  G06F9/30014 ,  G06F9/30185 ,  G06F9/3863 ,  G06F9/5044 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084 ,  G06N20/00 ,  G06F3/14 ,  G06T1/60 ,  G06T15/005

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：US11727246B2

公开(公告)日：2023-08-15

申请号：US16283021

申请日：2019-02-22

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 ,  G06N3/082 ,  G06N3/063 ,  G06T1/20 ,  G06N3/044 ,  G06N3/045

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

Abstract: Embodiments provide systems and methods which facilitate optimization of a convolutional neural network (CNN). One embodiment provides for a non-transitory machine-readable medium storing instructions that cause one or more processors to perform operations comprising processing a trained convolutional neural network (CNN) to generate a processed CNN, the trained CNN having weights in a floating-point format. Processing the trained CNN includes quantizing the weights in the floating-point format to generate weights in an integer format. Quantizing the weights includes generating a quantization table to enable non-uniform quantization of the weights and quantizing the weights from the floating-point format to the integer format using the quantization table. The operations additionally comprise performing an inference operation utilizing the processed CNN with the integer format weights.

公开(公告)号：US20230061331A1

公开(公告)日：2023-03-02

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

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：US20220327357A1

公开(公告)日：2022-10-13

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 ,  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：US20220004935A1

公开(公告)日：2022-01-06

申请号：US17481553

申请日：2021-09-22

Applicant: Intel Corporation

Inventor： Barath Lakshmanan ,  Craig Sperry ,  David Austin ,  Nilesh Ahuja

IPC: G06N20/20 ,  G06N7/00 ,  G06N3/04

Abstract: An apparatus to facilitate ensemble learning for deep feature defect detection is disclosed. The apparatus includes one or more processors to receive a deep feature vector from a feature extractor of an ensemble learning system, the deep feature vector extracted from input data; cluster the deep feature vector into a plurality of clusters based on a distance into the plurality of clusters; execute a probabilistic machine learning model corresponding to a cluster of the plurality of clusters to which the deep feature vector is clustered; and detect whether the deep feature vector comprises a defect based on an output of execution of the probabilistic machine learning model.

公开(公告)号：US20210397925A1

公开(公告)日：2021-12-23

申请号：US17446101

申请日：2021-08-26

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 ,  G06N3/08 ,  G06N3/063 ,  G06T1/20

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：US10929749B2

公开(公告)日：2021-02-23

申请号：US15494948

申请日：2017-04-24

Applicant: Intel Corporation

Inventor： Narayan Srinivasa ,  Joydeep Ray ,  Nicolas C. Galoppo Von Borries ,  Ben Ashbaugh ,  Prasoonkumar Surti ,  Feng Chen ,  Barath Lakshmanan ,  Elmoustapha Ould-Ahmed-Vall ,  Liwei Ma ,  Linda L. Hurd ,  Abhishek R. Appu ,  John C. Weast ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Chandrasekaran Sakthivel ,  Farshad Akhbari ,  Dukhwan Kim ,  Altug Koker ,  Nadathur Rajagopalan Satish

IPC: G06F9/30 ,  G06F9/38 ,  G06F9/28 ,  G06F9/302 ,  G06F15/00 ,  G06N3/08 ,  G06N3/04 ,  G06N3/063

Abstract: An apparatus to facilitate optimization of a neural network (NN) is disclosed. The apparatus includes optimization logic to define a NN topology having one or more macro layers, adjust the one or more macro layers to adapt to input and output components of the NN and train the NN based on the one or more macro layers.

公开(公告)号：US10853906B2

公开(公告)日：2020-12-01

申请号：US16197821

申请日：2018-11-21

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: One embodiment provides an accelerator module comprising a memory stack including multiple memory dies; a graphics processing unit (GPU) coupled with the memory stack via one or more memory controllers, the GPU including a plurality of multiprocessors having a single instruction, multiple thread (SIMT) architecture, the multiprocessors to execute at least one single instruction. The at least one single instruction is to cause at least a portion of the GPU to perform a floating point operation on input having differing precisions. The floating point operation is a two-dimensional matrix multiply and accumulate operation.