Dynamic thermal management for multimedia applications using machine learning

Yang Ge, Qinru Qiu

Research output: Chapter in Book/Entry/PoemConference contribution

39 Scopus citations


Multimedia applications are expected to form the largest portion of workload in general purpose PC and portable devices. The ever-increasing computation intensity of multimedia applications elevates the processor temperature and consequently impairs the reliability and performance of the system. In this paper, we propose to perform dynamic thermal management using reinforcement learning algorithm for multimedia applications. The proposed learning model does not need any prior knowledge of the workload information or the system thermal and power characteristics. It learns the temperature change and workload switching patterns by observing the temperature sensor and event counters on the processor, and finds the management policy that provides good performance-thermal tradeoff during the runtime. We validated our model on a Dell personal computer with Intel Core 2 processor. Experimental results show that our approach provides considerable performance improvements with marginal increase in the percentage of thermal hotspot comparing to existing workload phase detection approach.

Original languageEnglish (US)
Title of host publication2011 48th ACM/EDAC/IEEE Design Automation Conference, DAC 2011
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781450306362
StatePublished - 2011
Externally publishedYes

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X


  • Dynamic thermal management
  • multimedia application
  • reinforcement learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


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