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Supervised Learning Based Power Management for Multicore Processors

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2 Author(s)
Hwisung Jung ; Broadcom Corp., Irvine, CA, USA ; Pedram, M.

This paper presents a supervised learning based power management framework for a multi-processor system, where a power manager (PM) learns to predict the system performance state from some readily available input features (such as the occupancy state of a global service queue) and then uses this predicted state to look up the optimal power management action (e.g., voltage-frequency setting) from a precomputed policy table. The motivation for utilizing supervised learning in the form of a Bayesian classifier is to reduce the overhead of the PM which has to repetitively determine and assign voltage-frequency settings for each processor core in the system. Experimental results demonstrate that the proposed supervised learning based power management technique ensures system-wide energy savings under rapidly and widely varying workloads.

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Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on  (Volume:29 ,  Issue: 9 )