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:
Jan. 06, 2026

Filed:

Dec. 12, 2022
Applicant:

Moffett International Co., Limited, Hong Kong, HK;

Inventors:

Enxu Yan, Los Altos, CA (US);

Yong Lu, Los Altos, CA (US);

Wei Wang, Los Altos, CA (US);

Zhibin Xiao, Los Altos, CA (US);

Jiachao Liu, Los Altos, CA (US);

Hengchang Xiong, Shenzhen, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/30 (2018.01); G06F 9/38 (2018.01); G06N 3/0464 (2023.01);
U.S. Cl.
CPC ...
G06N 3/0464 (2023.01); G06F 9/30036 (2013.01); G06F 9/3877 (2013.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for parallelizing convolution processing. An exemplary method comprises: segmenting an input tensor into a plurality of sub-tensors and a plurality of filters into a plurality of sub-filter groups; respectively assigning a plurality of combinations of the sub-tensors and the sub-filter groups to a plurality of processors; storing, by each of the plurality of processors, non-zero values of the sub-tensor and the sub-filter group in the assigned combination as index-value pairs; parallelly performing for a plurality of iterations, by the plurality of processors, multiply-and-accumulate (MAC) operations based on the index-value pairs to obtain a plurality of outputs, where the index-value pairs of the sub-filter groups are rotated among the plurality of processors across the plurality of iterations; and aggregating the plurality of outputs as an output tensor.


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