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:

Jul. 27, 2018
Applicant:

Syntiant, Aliso Viejo, CA (US);

Inventors:

Kurt F. Busch, Laguna Hills, CA (US);

Jeremiah H. Holleman, III, Irvine, CA (US);

Pieter Vorenkamp, Laguna Beach, CA (US);

Stephen W. Bailey, Irvine, CA (US);

Assignee:

Syntiant, Aliso Viejo, CA (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/06 (2006.01); G06F 17/18 (2006.01); G06N 3/08 (2023.01); G06N 5/04 (2023.01); G06N 3/10 (2006.01); G06N 3/065 (2023.01); G06N 5/046 (2023.01);
U.S. Cl.
CPC ...
G06N 3/065 (2023.01); G06F 17/18 (2013.01); G06N 3/08 (2013.01); G06N 3/105 (2013.01); G06N 5/046 (2013.01);
Abstract

Provided herein is an integrated circuit including, in some embodiments, a hybrid neural network including a plurality of analog layers, a digital layer, and a plurality of data outputs. The plurality of analog layers is configured to include programmed weights of the neural network for decision making by the neural network. The digital layer, disposed between the plurality of analog layers and the plurality of data outputs, is configured for programming to compensate for weight drifts in the programmed weights of the neural network, thereby maintaining integrity of the decision making by the neural network. Also provided herein is a method including, in some embodiments, programming the weights of the plurality of analog layers; determining the integrity of the decision making by the neural network; and programming the digital layer of the neural network to compensate for the weight drifts in the programmed weights of the neural network.


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