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:
Oct. 08, 2024

Filed:

Sep. 18, 2023
Applicant:

Cambricon Technologies Corporation Limited, Beijing, CN;

Inventors:

Shaoli Liu, Beijing, CN;

Bingrui Wang, Beijing, CN;

Jun Liang, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/30 (2018.01); G06F 11/30 (2006.01); G06F 13/16 (2006.01); G06F 13/42 (2006.01); G06N 3/063 (2023.01);
U.S. Cl.
CPC ...
G06F 9/3004 (2013.01); G06F 9/3016 (2013.01); G06F 9/30192 (2013.01); G06F 11/3003 (2013.01); G06F 11/3055 (2013.01); G06F 13/1668 (2013.01); G06F 13/4282 (2013.01); G06F 2213/0026 (2013.01); G06N 3/063 (2013.01);
Abstract

The present disclosure provides a data processing method and an apparatus and a related product for increased efficiency of tensor processing. The products include a control module including an instruction caching unit, an instruction processing unit, and a storage queue unit. The instruction caching unit is configured to store computation instructions associated with an artificial neural network operation; the instruction processing unit is configured to parse the computation instructions to obtain a plurality of operation instructions; and the storage queue unit is configured to store an instruction queue, where the instruction queue includes a plurality of operation instructions or computation instructions to be executed in the sequence of the queue. By adopting the above-mentioned method, the present disclosure can improve the operation efficiency of related products when performing operations of a neural network model.


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