Adaptive Power Management Based on Reinforcement Learning for Embedded System

Authors:
Cheng-Ting Liu;Roy Chaoming Hsu
Affiliations:
Department of Computer Science and Information Engineering, National Chiayi University, Chiayi City, Taiwan, R.O.C. 60004;Department of Computer Science and Information Engineering, National Chiayi University, Chiayi City, Taiwan, R.O.C. 60004
Venue:
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Year:
2008

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Abstract

In this study, an adaptive power management method based on reinforcement learning is proposed to improve the energy utilization and battery endurance for resource-limited embedded systems. A simulator which traces battery endurance and device operations is developed to examine the proposed method. Experimental results show that, in terms of battery efficiency and endurance, the performance of our proposed method is better than the traditional power management techniques, such as static power management method.