PowerTrain: A learning-based calibration of McPAT power models

Abstract

As research on improving energy efficiency becomes prevalent, the necessity of a tool to accurately estimate power is increasing. Among various tools proposed, McPAT has gained some popularity due to its easy-to-use analytical power models. However, McPAT's prediction has several limitations. Although under- or over-estimated power from unmodeled and mis-modeled parts offset each other, it still incorporates errors in each block. Moreover, the lack of awareness to the implementation details exacerbates the prediction inaccuracies. To alleviate this problem, we propose a new methodology to train McPAT towards precise processor power prediction using power measurements from real hardware. This calibration enables McPAT's power to fit to the target processor power. Once we adjusted the power consumption of each block to best match those in the target processor, our trained McPAT delivered more precise power estimation. We calibrated the outputs of McPAT against a Cortex-A15 within a Samsung Exynos 5422 SoC. We observe that our methodology successfully reduces the errors, particularly for workloads with fluctuating power behaviors. The results show that the mean percentage error and the mean percentage absolute error of the calibrated power against real hardware are 2.04 percent and 4.37 percent, respectively.