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. 30, 2024

Filed:

Sep. 26, 2016
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Anbang Yao, Beijing, CN;

Yiwen Guo, Beijing, CN;

Lin Xu, Beijing, CN;

Yan Lin, Beijing, CN;

Yurong Chen, Beijing, CN;

Assignee:

INTEL CORPORATION, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06N 3/082 (2023.01); G06N 3/084 (2023.01); G06N 3/02 (2006.01); G06N 3/045 (2023.01); G06F 17/16 (2006.01); G06N 3/04 (2023.01); G06N 3/044 (2023.01);
U.S. Cl.
CPC ...
G06N 3/082 (2013.01); G06F 17/16 (2013.01); G06N 3/02 (2013.01); G06N 3/04 (2013.01); G06N 3/045 (2023.01); G06N 3/084 (2013.01); G06N 3/044 (2023.01);
Abstract

An apparatus and method are described for reducing the parameter density of a deep neural network (DNN). A layer-wise pruning module to prune a specified set of parameters from each layer of a reference dense neural network model to generate a second neural network model having a relatively higher sparsity rate than the reference neural network model; a retraining module to retrain the second neural network model in accordance with a set of training data to generate a retrained second neural network model; and the retraining module to output the retrained second neural network model as a final neural network model if a target sparsity rate has been reached or to provide the retrained second neural network model to the layer-wise pruning model for additional pruning if the target sparsity rate has not been reached.


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