-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.Index Terms -Bayesian classification, dynamic power management, machine learning, multi-processor system, supervised learning

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Abstract1 . This paper presents a supervised learning based dynamic

Achieving high performance under a peak temperature limit is a first-order concern for VLSI designers. This paper presents a new abstract model of a thermally-managed system, where a stochastic process model is employed to capture the system performance and thermal behavior. We formulate the problem of dynamic thermal management (DTM) as the problem of minimizing the energy cost of the system for a given level of performance under a peak temperature constraint by using a controllable Markovian decision process (MDP) model. The key rationale for utilizing MDP for solving the DTM problem is to manage the stochastic behavior of the temperature states of the system under online re-configuration of its micro-architecture and/or dynamic voltage-frequency scaling. Experimental results demonstrate the effectiveness of the modeling framework and the proposed DTM technique.