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:
Apr. 08, 2025

Filed:

May. 21, 2019
Applicant:

Institute of Computing Technology, Chinese Academy of Sciences, Beijing, CN;

Inventors:

Xiaowei Li, Beijing, CN;

Xin Wei, Beijing, CN;

Hang Lu, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/048 (2023.01); G06F 5/01 (2006.01); G06F 7/50 (2006.01); G06F 7/544 (2006.01); G06F 17/16 (2006.01); G06N 3/04 (2023.01); G06N 3/063 (2023.01); H03M 7/40 (2006.01);
U.S. Cl.
CPC ...
G06N 3/048 (2023.01); G06F 5/01 (2013.01); G06F 7/50 (2013.01); G06F 7/5443 (2013.01); G06F 17/16 (2013.01); G06N 3/04 (2013.01); G06N 3/063 (2013.01); H03M 7/40 (2013.01); G06F 2207/386 (2013.01);
Abstract

Disclosed embodiments relate to a convolutional neural network computing method and system based on weight kneading, comprising: arranging original weights in a computation sequence and aligning by bit to obtain a weight matrix, removing slack bits in the weight matrix, allowing essential bits in each column of the weight matrix to fill the vacancies according to the computation sequence to obtain an intermediate matrix, removing null rows in the intermediate matrix, obtain a kneading matrix, wherein each row of the kneading matrix serves as a kneading weight; obtaining positional information of the activation corresponding to each bit of the kneading weight; divides the kneading weight by bit into multiple weight segments, processing summation of the weight segments and the corresponding activations according to the positional information, and sending a processing result to an adder tree to obtain an output feature map by means of executing shift-and-add on the processing result.


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