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:
Jul. 07, 2026

Filed:

Sep. 26, 2022
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Alessandro Palla, Pisa, IT;

David Thomas Bernard, Kilcullen, IE;

Niall Hanrahan, Galway, IE;

Assignee:

Intel Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 17/15 (2006.01); G06F 40/00 (2020.01); G06F 40/30 (2020.01); G06F 40/35 (2020.01); G06F 40/40 (2020.01); G06N 3/00 (2023.01); G06N 3/042 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 3/084 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 17/15 (2013.01); G06F 40/30 (2020.01); G06F 40/35 (2020.01); G06F 40/40 (2020.01); G06N 3/042 (2023.01); G06N 3/045 (2023.01); G06N 3/084 (2013.01); G06N 20/00 (2019.01);
Abstract

A deconvolution can be decomposed into multiple convolutions. Results of the convolutions constitute an output of the deconvolution. Zeros may be added to an input tensor of the deconvolution to generate an upsampled input tensor. Subtensors having the same size as the kernel of the deconvolution may be identified from the upsampled input tensor. A subtensor may include one or more input activations and one or more zeros. Subtensors having same distribution patterns of input activations may be used to generate a reduced kernel. The reduced kernel includes a subset of the kernel. The position of a weight in the reduced kernel may be the same as the positions of an input activation in the subtensor. Multiple reduced kernels may be generated based on multiple subtensors having different distribution patterns of activations. Each of the convolutions may use the input tensor and a different one of the reduced kernels.


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