A Statistical Approach to Power Estimation for x86 Processors

With the growing significance of green computing and difficulty in obtaining accurate real time power measurements, there is an increasing need for accurate and reliable power estimation techniques for energy-aware performance optimization. In this paper we present a statistical approach for building accurate power models using Performance Monitoring Counters (PMC) as effective proxies for x86 systems. The selection of PMC events is based on statistical methods described in literature for ARM systems. The models are trained and validated through a synthetic workload generator as well as standardized benchmarks, using k-fold cross validation technique. We demonstrate the accuracy of the resultant models across different voltage and frequency states using sophisticated reference measurements. Furthermore, we analyze the significance of the chosen PMC events, which form the framework for the regression based power models.