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:
Apr. 02, 2024

Filed:

Dec. 04, 2020
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Ouais Alsharif, Mountain View, CA (US);

Rohit Prakash Prabhavalkar, Santa Clara, CA (US);

Ian C. McGraw, Menlo Park, CA (US);

Antoine Jean Bruguier, Milpitas, CA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/044 (2023.01); G06N 3/049 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06F 17/16 (2006.01); G06N 3/04 (2023.01); G06N 3/084 (2023.01);
U.S. Cl.
CPC ...
G06N 3/044 (2023.01); G06N 3/049 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G05B 2219/33025 (2013.01); G05B 2219/40326 (2013.01); G06F 17/16 (2013.01); G06N 3/04 (2013.01); G06N 3/084 (2013.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a compressed recurrent neural network (RNN). One of the systems includes a compressed RNN, the compressed RNN comprising a plurality of recurrent layers, wherein each of the recurrent layers has a respective recurrent weight matrix and a respective inter-layer weight matrix, and wherein at least one of recurrent layers is compressed such that a respective recurrent weight matrix of the compressed layer is defined by a first compressed weight matrix and a projection matrix and a respective inter-layer weight matrix of the compressed layer is defined by a second compressed weight matrix and the projection matrix.


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