Research On System-Level Dynamic Power Management Using Learning Neural Networks

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 systemlevel power management policies. We proposed two PM policiesBack 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、1.18competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.