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. 29, 2025

Filed:

Jan. 18, 2024
Applicant:

Groq, Inc., Mountain View, CA (US);

Inventor:

Sushma Honnavara-Prasad, San Jose, CA (US);

Assignee:

Groq, Inc., Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 1/26 (2006.01); G06F 1/28 (2006.01); G06F 1/32 (2019.01); G06F 1/329 (2019.01); G06F 9/48 (2006.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
G06F 1/329 (2013.01); G06F 1/28 (2013.01); G06F 9/4893 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01);
Abstract

In one embodiment, the present disclosure includes a method of reducing power in an artificial intelligence processor. For each cycle, over a plurality of cycles, an AI model is translated into operations executable on an artificial intelligence processor. The translating is based on power parameters that correspond to power consumption and performance of the artificial intelligence processor. The AI processor is configured with the executable operations, and input activation data sets are processed. Accordingly, result sets, power consumption data, and performance data are generated and stored over the plurality of cycles. The method further includes training an AI algorithm using the stored parameters, the power consumption data, and the performance data. A trained AI algorithm outputs a plurality of optimized parameters to reduce power consumption of the AI processor. The AI model is then translated into optimized executable operations based on the plurality of optimized parameters.


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