公开(公告)号：US20180373981A1

公开(公告)日：2018-12-27

申请号：US16014869

申请日：2018-06-21

Applicant: TuSimple

Inventor： Yuwei HU ,  Jiangming JIN ,  Lei SU ,  Dinghua LI

IPC: G06N3/08 ,  G06F17/15 ,  H03M7/30 ,  G06F12/02

Abstract: The embodiments of this application provide a method and device for optimizing neural network. The method includes: binarizing and bit-packing input data of a convolution layer along a channel direction, and obtaining compressed input data; binarizing and bit-packing respectively each convolution kernel of the convolution layer along the channel direction, and obtaining each corresponding compressed convolution kernel; dividing the compressed input data sequentially in a convolutional computation order into blocks of the compressed input data with the same size of each compressed convolution kernel, wherein the data input to one time convolutional computation form a data block; and, taking a convolutional computation on each block of the compressed input data and each compressed convolution kernel sequentially, obtaining each convolutional result data, and obtaining multiple output data of the convolution layer according to each convolutional result data.

公开(公告)号：US20230177336A1

公开(公告)日：2023-06-08

申请号：US18162871

申请日：2023-02-01

Applicant: TuSimple, Inc. ,  Beijing Tusen Zhitu Technology Co., Ltd.

Inventor： Yuwei HU ,  Jiangming JIN ,  Lei SU ,  Dinghua LI

IPC: G06N3/08 ,  G06F12/02 ,  H03M7/30 ,  G06F17/15 ,  G06N3/063 ,  G06F17/16 ,  G06N20/10 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06F12/0207 ,  H03M7/30 ,  G06F17/153 ,  G06N3/063 ,  G06F17/16 ,  G06N20/10 ,  G06N3/045

Abstract: The embodiments of this application provide a method and device for optimizing neural network. The method includes: binarizing and bit-packing input data of a convolution layer along a channel direction, and obtaining compressed input data; binarizing and bit-packing respectively each convolution kernel of the convolution layer along the channel direction, and obtaining each corresponding compressed convolution kernel; dividing the compressed input data sequentially in a convolutional computation order into blocks of the compressed input data with the same size of each compressed convolution kernel, wherein the data input to one time convolutional computation form a data block; and, taking a convolutional computation on each block of the compressed input data and each compressed convolution kernel sequentially, obtaining each convolutional result data, and obtaining multiple output data of the convolution layer according to each convolutional result data.