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. 21, 2025

Filed:

Jul. 15, 2023
Applicant:

Mythic, Inc., Austin, TX (US);

Inventors:

David Fick, Hutto, TX (US);

Daniel Graves, Beaumont, CA;

Michael Klachko, Boca Raton, FL (US);

Ilya Perminov, Vancouver, CA;

John Mark Beardslee, Menlo Park, CA (US);

Evgeny Shapiro, Vancouver, CA;

Assignee:

Mythic, Inc., Austin, TX (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/063 (2023.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
G06N 3/063 (2013.01); G06N 3/08 (2013.01);
Abstract

A system and method for enhancing inferential accuracy of an artificial neural network during training includes during a simulated training of an artificial neural network identifying channel feedback values of a plurality of distinct channels of a layer of the artificial neural network based on an input of a training batch; if the channel feedback values do not satisfy a channel signal range threshold, computing a channel equalization factor based on the channel feedback values; identifying a layer feedback value based on the input of the training batch; and if the layer feedback value does not satisfy a layer signal range threshold, identifying a composite scaling factor based on the layer feedback values; during a non-simulated training of the artificial neural network, providing training inputs of: the training batch; the composite scaling factor; the channel equalization factor; and training the artificial neural network based on the training inputs.


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