The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 14, 2023

Filed:

Jun. 21, 2018
Applicants:

Tusimple, Inc., San Diego, CA (US);

Beijing Tusen Zhitu Technology Co., Ltd., Beijing, CN;

Inventors:

Yuwei Hu, Beijing, CN;

Jiangming Jin, Beijing, CN;

Lei Su, Beijing, CN;

Dinghua Li, Beijing, CN;

Assignees:

TU SIMPLE, INC., San Diego, CA (US);

BEIJING TUSEN ZHITU TECHNOLOGY CO., LTD., Beijing, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/15 (2006.01); G06N 3/08 (2023.01); G06F 12/02 (2006.01); H03M 7/30 (2006.01); G06N 3/063 (2023.01); G06N 3/04 (2023.01); G06F 17/16 (2006.01); G06N 20/10 (2019.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 12/0207 (2013.01); G06F 17/153 (2013.01); G06F 17/16 (2013.01); G06N 3/0454 (2013.01); G06N 3/063 (2013.01); G06N 20/10 (2019.01); H03M 7/30 (2013.01);
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.


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