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:
May. 23, 2023

Filed:

Sep. 16, 2019
Applicant:

Qualcomm Incorporated, San Diego, CA (US);

Inventors:

Jamie Menjay Lin, San Diego, CA (US);

Yang Yang, San Diego, CA (US);

Jilei Hou, San Diego, CA (US);

Assignee:

Qualcomm Incorporated, San Diego, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2023.01); G06F 17/15 (2006.01); G06F 17/16 (2006.01); G06K 9/62 (2022.01); G06N 3/08 (2023.01); G06T 7/70 (2017.01); G06V 40/10 (2022.01); G06N 3/084 (2023.01);
U.S. Cl.
CPC ...
G06N 3/0472 (2013.01); G06F 17/15 (2013.01); G06F 17/16 (2013.01); G06K 9/6262 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06K 9/6267 (2013.01); G06T 7/70 (2017.01); G06T 2207/20084 (2013.01); G06V 40/10 (2022.01);
Abstract

Embodiments described herein relate to a method, comprising: receiving input data at a convolutional neural network (CNN) model; generating a factorized computation network comprising a plurality of connections between a first layer of the CNN model and a second layer of the CNN model, wherein: the factorized computation network comprises N inputs, the factorized computation network comprises M outputs, and the factorized computation network comprises at least one path from every input of the N inputs to every output of the M outputs; setting a connection weight for a plurality of connections in the factorized computation network to 1 so that a weight density for the factorized computation network is <100%; performing fast pointwise convolution using the factorized computation network to generate fast pointwise convolution output; and providing the fast pointwise convolution output to the second layer of the CNN model.


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