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:
Sep. 05, 2023

Filed:

Sep. 08, 2020
Applicant:

Socionext Inc., Kanagawa, JP;

Inventor:

Yukihiro Sasagawa, Yokohama, JP;

Assignee:

SOCIONEXT INC., Kanagawa, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/063 (2023.01); G06N 3/08 (2023.01); G06N 20/10 (2019.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06F 18/2451 (2023.01); G06F 18/2453 (2023.01);
U.S. Cl.
CPC ...
G06N 3/063 (2013.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06F 18/2451 (2023.01); G06F 18/2453 (2023.01); G06N 3/08 (2013.01); G06N 20/10 (2019.01);
Abstract

A quantization parameter optimization method includes: determining a cost function in which a regularization term is added to an error function, the regularization term being a function of a quantization error that is an error between a weight parameter of a neural network and a quantization parameter that is a quantized weight parameter; updating the quantization parameter by use of the cost function; and determining, as an optimized quantization parameter of a quantization neural network, the quantization parameter with which a function value derived from the cost function satisfies a predetermined condition, the optimized quantization parameter being obtained as a result of repeating the updating, the quantization neural network being the neural network, the weight parameter of which has been quantized, wherein the function value derived from the regularization term and an inference accuracy of the quantization neural network are negatively correlated.


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