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:
Mar. 12, 2024

Filed:

Feb. 09, 2018
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Yair Alon, Mountain View, CA (US);

Elad Eban, Sunnyvale, CA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/084 (2023.01); G06N 3/044 (2023.01); G06N 3/08 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06N 3/063 (2023.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G06N 3/044 (2023.01); G06N 3/08 (2013.01); G06N 7/01 (2023.01); G06N 3/063 (2013.01); G06N 20/00 (2019.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for neural network compression. In one aspect, a method comprises receiving a neural network and identifying a particular set of multiple weights of the neural network. Multiple anchor points are determined based on current values of the particular set of weights of the neural network. The neural network is trained by, at each of multiple training iterations, performing operations comprising adjusting the values of the particular set of weights by backpropagating gradients of a loss function. The loss function comprises a first loss function term based on a prediction accuracy of the neural network and a second loss function term based on a similarity of the current values of the particular set of weights to the anchor points. After training, the values of the particular set of weights are quantized based on the anchor points.


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