SOC dynamic power management using artificial neural network

Authors:
Huaxiang Lu;Yan Lu;Zhifang Tang;Shoujue Wang
Affiliations:
Neural Network Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China;Neural Network Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China;Neural Network Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China;Neural Network Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

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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.