SOC dynamic power management using artificial neural network
Lu, HX (Lu, Huaxiang); Lu, Y (Lu, Yan); Tang, ZF (Tang, Zhifang); Wang, SJ (Wang, Shoujue); Lu, HX, Chinese Acad Sci, Inst Semicond, Neural Network Lab, Beijing 100083, Peoples R China.
2006
会议名称6th International Conference on Intelligent Systems Design and Applications (ISDA 2006)
会议录名称ISDA 2006 Sixth International Conference on Intelligent Systems Design and Applications
页码Vol 1: 133-137
会议日期OCT 16-18, 2006
会议地点Jinan, PEOPLES R CHINA
出版地10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
出版者IEEE COMPUTER SOC
ISBN0-7695-2528-8
部门归属chinese acad sci, inst semicond, neural network lab, beijing 100083, peoples r china
摘要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.
关键词Power Management Bp Rbf
学科领域人工智能
主办者IEEE Syst Man & Cybernet Soc. Jinan Univ
收录类别CPCI-S
语种英语
文献类型会议论文
条目标识符http://ir.semi.ac.cn/handle/172111/9768
专题中国科学院半导体研究所(2009年前)
通讯作者Lu, HX, Chinese Acad Sci, Inst Semicond, Neural Network Lab, Beijing 100083, Peoples R China.
推荐引用方式
GB/T 7714
Lu, HX ,Lu, Y ,Tang, ZF ,et al. SOC dynamic power management using artificial neural network[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:IEEE COMPUTER SOC,2006:Vol 1: 133-137.
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