DYNAMIC ENERGY PERFORMANCE PREFERENCE BASED ON WORKLOADS USING AN ADAPTIVE ALGORITHM

Described are mechanisms and methods for tracking user behavior profile over large time intervals and extracting observations for a user usage profile. The mechanisms and methods use machine learning (ML) algorithms embedded into a dynamic platform and thermal framework (DPTF) (e.g., Dynamic Tuning...

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Bibliographic Details
Main Authors BEJA, Hadas, HERMERDING, II, James, NAIR, Sudheer, PERI, Moran, WEISSMANN, Eliezer, YASSIN, Noha, OLSWANG, Gilad, ROTEM, Efraim, SAKARDA, Premanand, GANAPATHY, Deepak, FENGER, Russell, WAGNER, Avishai, ABU SALAH, Hisham, KRISHNAKUMAR, Nivedha, KARAVANY, Ido, WANG, Zhongsheng
Format Patent
LanguageEnglish
Published 16.06.2022
Subjects
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:Described are mechanisms and methods for tracking user behavior profile over large time intervals and extracting observations for a user usage profile. The mechanisms and methods use machine learning (ML) algorithms embedded into a dynamic platform and thermal framework (DPTF) (e.g., Dynamic Tuning Technology) and predict device workloads using hardware (HW) counters. These mechanisms and methods may accordingly increase performance and user responsiveness by dynamically changing an Energy Performance Preference (EPP) based on a longer time workload analysis and workload prediction.
Bibliography:Application Number: US202017442374