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In this paper, the authors propose a novel online-learning algorithm for system-level power management. They formulate both Dynamic Power Management (DPM) and dynamic voltage frequency scaling problems as one of workload characterization and selection and solve them using the algorithm. The selection is done among a set of experts, which refers to a set of DPM policies and voltage-frequency settings, leveraging the fact that different experts outperform each other under different workloads and device leakage characteristics. The online-learning algorithm adapts to changes in the characteristics and guarantees fast convergence to the best-performing expert.
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