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
Link To Document :
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