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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Main Authors | , , , , , , , , , , , , , , , |
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Format | Patent |
Language | English French German |
Published |
02.08.2023
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Subjects | |
Online Access | Get full text |
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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. |
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Bibliography: | Application Number: EP20200841091 |