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:
Sep. 06, 2022

Filed:

Dec. 28, 2017
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Alexander Bachmutsky, Sunnyvale, CA (US);

Kshitij A. Doshi, Tempe, AZ (US);

Francesc Guim Bernat, Barcelona, ES;

Raghu Kondapalli, San Jose, CA (US);

Suraj Prabhakaran, Aachen, DE;

Assignee:

Intel Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); H04L 67/1097 (2022.01); G06N 3/08 (2006.01); H04L 67/125 (2022.01); H04L 67/12 (2022.01); H04L 67/10 (2022.01); G06N 3/063 (2006.01); H04W 4/38 (2018.01);
U.S. Cl.
CPC ...
G06K 9/6256 (2013.01); G06N 3/063 (2013.01); G06N 3/08 (2013.01); G06N 3/082 (2013.01); H04L 67/10 (2013.01); H04L 67/1097 (2013.01); H04L 67/12 (2013.01); H04L 67/125 (2013.01); H04W 4/38 (2018.02);
Abstract

An apparatus for training artificial intelligence (AI) models is presented. In embodiments, the apparatus may include an input interface to receive in real time model training data from one or more sources to train one or more artificial neural networks (ANNs) associated with the one or more sources, each of the one or more sources associated with at least one of the ANNs; a load distributor coupled to the input interface to distribute in real time the model training data for the one or more ANNs to one or more AI appliances; and a resource manager coupled to the load distributor to dynamically assign one or more computing resources on ones of the AI appliances to each of the ANNs in view of amounts of the training data received in real time from the one or more sources for their associated ANNs.


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