公开(公告)号：US20210192337A1

公开(公告)日：2021-06-24

申请号：US16724849

申请日：2019-12-23

Applicant: Arm Limited

Inventor： Danny Daysang Loh ,  Lingchuan Meng ,  Naveen Suda ,  Eric Kunze ,  Ahmet Fatih Inci

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

Abstract: The present disclosure advantageously provides a heterogenous system, and a method for generating an artificial neural network (ANN) for a heterogenous system. The heterogenous system includes a plurality of processing units coupled to a memory configured to store an input volume. The plurality of processing units includes first and second processing units. The first processing unit includes a first processor and is configured to execute a first ANN, and the second processing unit includes a second processor and is configured to execute a second ANN. The first and second ANNs respectively include an input layer, at least one processor-optimized hidden layer and an output layer. The second ANN hidden layers are different than the first ANN hidden layers.

公开(公告)号：US11620516B2

公开(公告)日：2023-04-04

申请号：US16724849

申请日：2019-12-23

Applicant: Arm Limited

Inventor： Danny Daysang Loh ,  Lingchuan Meng ,  Naveen Suda ,  Eric Kunze ,  Ahmet Fatih Inci

IPC: G06N3/08 ,  G06N20/10 ,  G06N3/063 ,  G06N3/04 ,  G06N20/00 ,  G06N20/20

Abstract: The present disclosure advantageously provides a heterogenous system, and a method for generating an artificial neural network (ANN) for a heterogenous system. The heterogenous system includes a plurality of processing units coupled to a memory configured to store an input volume. The plurality of processing units includes first and second processing units. The first processing unit includes a first processor and is configured to execute a first ANN, and the second processing unit includes a second processor and is configured to execute a second ANN. The first and second ANNs respectively include an input layer, at least one processor-optimized hidden layer and an output layer. The second ANN hidden layers are different than the first ANN hidden layers.

公开(公告)号：US11449729B2

公开(公告)日：2022-09-20

申请号：US16676757

申请日：2019-11-07

Applicant: Arm Limited

Inventor： Lingchuan Meng ,  Danny Daysang Loh ,  Ian Rudolf Bratt ,  Alexander Eugene Chalfin ,  Tianmu Li

IPC: G06N3/04 ,  G06N3/06 ,  G06N3/08 ,  G06N3/10 ,  G06F17/15 ,  G06F17/16 ,  G06F17/18 ,  G06F30/18 ,  G06F30/20 ,  G06F30/27 ,  G06F30/33 ,  G06F30/367

Abstract: The present disclosure advantageously provides a system and a method for convolving data in a quantized convolutional neural network (CNN). The method includes selecting a set of complex interpolation points, generating a set of complex transform matrices based, at least in part, on the set of complex interpolation points, receiving an input volume from a preceding layer of the quantized CNN, performing a complex Winograd convolution on the input volume and at least one filter, using the set of complex transform matrices, to generate an output volume, and sending the output volume to a subsequent layer of the quantized CNN.

公开(公告)号：US11948069B2

公开(公告)日：2024-04-02

申请号：US16518444

申请日：2019-07-22

Applicant: Arm Limited

Inventor： Lingchuan Meng ,  John Wakefield Brothers, III ,  Jens Olson ,  Jared Corey Smolens ,  Eric Kunze ,  Ian Rudolf Bratt

IPC: G06N3/063 ,  H03M7/30

CPC classification number: G06N3/063 ,  H03M7/70

Abstract: A processor arranged to compress neural network activation data comprising an input module for obtaining neural network activation data. The processor also comprises a block creation module arranged to split the neural network activation data into a plurality of blocks; and a metadata generation module for generating metadata associated with at least one of the plurality of blocks. Based on the metadata generated a selection module selects a compression scheme for each of the plurality of blocks, and a compression module for applying the selected compression scheme to the corresponding block to produce compressed neural network activation data. An output module is also provided for outputting the compressed neural network activation data.

公开(公告)号：US20230196093A1

公开(公告)日：2023-06-22

申请号：US17559163

申请日：2021-12-22

Applicant: Arm Limited

Inventor： Kartikeya Bhardwaj ,  Naveen Suda ,  Lingchuan Meng ,  Alexander Eugene Chalfin ,  Danny Daysang Log

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Disclosed is a novel neural network architecture and methods for generating neural network-based models from such architecture. A first version of the neural network, that is used for training purposes, includes one or more blocks in a first format that can then be replaced with corresponding blocks in a second format for execution. An executable model can thus be provided comprising a second version of the neural network including the one or more blocks in the second format. This then allows the training to be performed in a first, e.g. expanded format, but with a second, e.g. reduced, format model then provided for execution.

公开(公告)号：US20200151541A1

公开(公告)日：2020-05-14

申请号：US16676757

申请日：2019-11-07

Applicant: Arm Limited

Inventor： Lingchuan Meng ,  Danny Daysang Loh ,  Ian Rudolf Bratt ,  Alexander Eugene Chalfin ,  Tianmu Li

IPC: G06N3/04 ,  G06F17/16 ,  G06F17/15

Abstract: The present disclosure advantageously provides a system and a method for convolving data in a quantized convolutional neural network (CNN). The method includes selecting a set of complex interpolation points, generating a set of complex transform matrices based, at least in part, on the set of complex interpolation points, receiving an input volume from a preceding layer of the quantized CNN, performing a complex Winograd convolution on the input volume and at least one filter, using the set of complex transform matrices, to generate an output volume, and sending the output volume to a subsequent layer of the quantized CNN.