SOC Dynamic Power Management Using Artificial Neural Network

  • Huaxiang Lu
  • Yan Lu
  • Zhifang Tang
  • Shoujue Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques — BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79, 1.45, 1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.


Artificial Neural Network Idle Time Power Management Idle Period Back Propagation Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huaxiang Lu
    • 1
  • Yan Lu
    • 1
  • Zhifang Tang
    • 1
  • Shoujue Wang
    • 1
  1. 1.Neural Network LaboratoryInstitute of Semiconductors, Chinese Academy of SciencesBeijingChina

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