Adaptive power management using reinforcement learning

Authors:
Ying Tan;Wei Liu;Qinru Qiu
Affiliations:
Binghamton University, State University of New York, Binghamton, New York;Binghamton University, State University of New York, Binghamton, New York;Binghamton University, State University of New York, Binghamton, New York
Venue:
Proceedings of the 2009 International Conference on Computer-Aided Design
Year:
2009

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Abstract

System level power management must consider the uncertainty and variability that comes from the environment, the application and the hardware. A robust power management technique must be able to learn the optimal decision from past history and improve itself as the environment changes. This paper presents a novel online power management technique based on model-free constrained reinforcement learning (RL). It learns the best power management policy that gives the minimum power consumption for a given performance constraint without any prior information of workload. Compared with existing machine learning based power management techniques, the RL based learning is capable of exploring the trade-off in the power-performance design space and converging to a better power management policy. Experimental results show that the proposed RL based power management achieves 24% and 3% reduction in power and latency respectively comparing to the existing expert based power management.