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. 29, 2022

Filed:

Jun. 12, 2020
Applicant:

Disney Enterprises, Inc., Burbank, CA (US);

Inventors:

Miquel Angel Farre Guiu, Bern, CH;

Marc Junyent Martin, Barcelona, ES;

Jordi Badia Pujol, Madrid, ES;

Assignee:

Disney Enterprises, Inc., Burbank, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/50 (2006.01); G06N 3/063 (2006.01); G06F 9/48 (2006.01); G06F 9/445 (2018.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06F 9/5016 (2013.01); G06F 9/44505 (2013.01); G06F 9/4881 (2013.01); G06F 9/5072 (2013.01); G06N 3/0454 (2013.01); G06N 3/063 (2013.01);
Abstract

An automated hardware resource optimization system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor is configured to execute the software code to identify computing hardware for hosting a neural network based application, determine, based on a first performance parameter of the computing hardware, a batch size for performing data processing using the neural network based application, and tune, using a data batch having the determined batch size, a second performance parameter of the computing hardware to enable substantially continuous loading of its hardware processor memory. The software code also optimizes, based on the determined batch size and the tuned second performance parameter, a process flow for performing the data processing, and generates a configuration file identifying the computing hardware, the neural network based application, the determined batch size, the tuned second performance parameter, and the optimized process flow.


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