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. 25, 2023

Filed:

Sep. 28, 2018
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Kooi Chi Ooi, Bukit Gambir, MY;

Min Suet Lim, Gelugor, MY;

Denica Larsen, Portland, OR (US);

Lady Nataly Pinilla Pico, El Dorado Hills, CA (US);

Divya Vijayaraghavan, Los Altos, CA (US);

Assignee:

INTEL CORPORATION, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 5/04 (2023.01); G06N 3/063 (2023.01); G06F 15/78 (2006.01); G06F 1/16 (2006.01); G06N 20/00 (2019.01); G06F 16/00 (2019.01); G06N 3/084 (2023.01); G06V 10/94 (2022.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06F 18/2413 (2023.01); G06N 3/048 (2023.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06N 3/063 (2013.01); G06F 1/163 (2013.01); G06F 15/7892 (2013.01); G06F 16/00 (2019.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06F 18/24143 (2023.01); G06N 3/045 (2023.01); G06N 3/048 (2023.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 10/955 (2022.01);
Abstract

Methods, apparatus, systems, and articles of manufacture are disclosed to improve data training of a machine learning model using a field-programmable gate array (FPGA). An example system includes one or more computation modules, each of the one or more computation modules associated with a corresponding user, the one or more computation modules training first neural networks using data associated with the corresponding users, and FPGA to obtain a first set of parameters from each of the one or more computation modules, the first set of parameters associated with the first neural networks, configure a second neural network based on the first set of parameters, execute the second neural network to generate a second set of parameters, and transmit the second set of parameters to the first neural networks to update the first neural networks.


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