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:
Nov. 15, 2022

Filed:

Nov. 28, 2018
Applicant:

Nanjing Horizon Robotics Technology Co., Ltd., Nanjing, CN;

Inventors:

Delin Li, Beijing, CN;

Kun Ling, Beijing, CN;

Liang Chen, Beijing, CN;

Jianjun Li, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/15 (2006.01); G06N 20/10 (2019.01); G06F 7/501 (2006.01); G06F 7/523 (2006.01); G06N 3/04 (2006.01); G06N 3/063 (2006.01);
U.S. Cl.
CPC ...
G06F 17/153 (2013.01); G06F 7/501 (2013.01); G06F 7/523 (2013.01); G06N 3/0454 (2013.01); G06N 3/063 (2013.01); G06N 20/10 (2019.01);
Abstract

Disclosed are a method and an apparatus for performing convolution operation on folded feature data. The method comprises: reading the folded feature data provided to a convolution layer and an original convolution kernel from a dynamic random access memory (DRAM); pre-processing the folded feature data and the original convolution kernel; storing the pre-processed folded feature data into a static random-access memory (SRAM); folding the pre-processed original convolution kernel in at least one dimension of width or height according to a folding manner of the folded feature data to generate one or more folded convolution kernels corresponding to the original convolution kernel; storing the one or more folded convolution kernels in the SRAM; and reading the pre-processed folded feature data and the one or more folded convolution kernels from the SRAM into a calculation unit for convolving the pre-processed folded feature data with the one or more folded convolution kernels.


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