• DocumentCode
    2794053
  • Title

    SOC Dynamic Power Management Using Artificial Neural Network

  • Author

    Lu, Huaxiang ; Lu, Yan ; Tang, Zhifang ; Wang, Shoujue

  • Author_Institution
    Neural Network Lab., Chinese Acad. of Sci., Beijing
  • Volume
    1
  • fYear
    2006
  • fDate
    16-18 Oct. 2006
  • Firstpage
    133
  • Lastpage
    137
  • Abstract
    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
  • Keywords
    backpropagation; integrated circuit modelling; low-power electronics; radial basis function networks; system-on-chip; SOC dynamic power management; artificial neural network; back propagation power management; radial basis function power management; system-level power consumption; Artificial neural networks; Delay; Energy consumption; Energy management; Laboratories; Neural networks; Power dissipation; Power system management; Stochastic processes; Uncertainty; Power Management- ABP- ARBF.;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
  • Conference_Location
    Jinan
  • Print_ISBN
    0-7695-2528-8
  • Type

    conf

  • DOI
    10.1109/ISDA.2006.245
  • Filename
    4021423