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. 21, 2023

Filed:

Feb. 20, 2020
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Steve Esser, San Jose, CA (US);

Jeffrey L. McKinstry, San Jose, CA (US);

Deepika Bablani, San Jose, CA (US);

Rathinakumar Appuswamy, San Jose, CA (US);

Dharmendra S. Modha, San Jose, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/16 (2006.01); G06F 17/10 (2006.01); G06N 3/02 (2006.01); G06N 3/084 (2023.01); G06N 3/04 (2023.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G06F 17/10 (2013.01); G06F 17/16 (2013.01); G06N 3/02 (2013.01); G06N 3/04 (2013.01);
Abstract

Learned step size quantization in artificial neural network is provided. In various embodiments, a system comprises an artificial neural network and a computing node. The artificial neural network comprises: a quantizer having a configurable step size, the quantizer adapted to receive a plurality of input values and quantize the plurality of input values according to the configurable step size to produce a plurality of quantized input values, at least one matrix multiplier configured to receive the plurality of quantized input values from the quantizer and to apply a plurality of weights to the quantized input values to determine a plurality of output values having a first precision, and a multiplier configured to scale the output values to a second precision. The computing node is operatively coupled to the artificial neural network and is configured to: provide training input data to the artificial neural network, and optimize the configurable step size based on a gradient through the quantizer and the training input data.


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