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:
May. 12, 2020

Filed:

Jan. 26, 2017
Applicant:

Ati Technologies Ulc, Markham, CA;

Inventors:

Soon Kyu Kwon, North York, CA;

Jun Huang, Richmond Hill, CA;

Shahriar Pezeshgi, Markham, CA;

Alexander Sabino Duenas, Stouffville, CA;

Assignee:

ATI TECHNOLOGIES ULC, Markham, CA;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 1/3234 (2019.01); G06F 1/3206 (2019.01); G06F 1/324 (2019.01); G06F 1/3218 (2019.01); G06T 1/20 (2006.01);
U.S. Cl.
CPC ...
G06F 1/3234 (2013.01); G06F 1/3206 (2013.01); G06T 1/20 (2013.01); G06F 1/324 (2013.01); G06F 1/3218 (2013.01); G06F 1/3265 (2013.01); Y02D 10/126 (2018.01);
Abstract

A GPU performs dynamic power level management by switching between pre-defined power levels having distinct clock and voltage levels. The dynamic power level management includes identifying a first performance metric associated with processing workloads at the for a consecutive number of measurement cycles. In some embodiments, the consecutive number of measurement cycles includes a current measurement cycle and at least one previous measurement cycle. Based on a determination that the consecutive number of measurement cycles exceeds a minimum hysteresis number, an estimated optimization is determined to be applied to the GPU for a future measurement cycle. A power level setting at the GPU for the future measurement cycle is adjusted based on the estimated optimization. By considering performance metrics including, for example, different processing workloads and hardware configurations, the GPU is able to dynamically adapt its power settings to the particular workload that it is currently processing.


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