McPAT-Calib: A Microarchitecture Power Modeling Framework for Modern CPUs
Jianwang Zhai, Chen Bai, Binwu Zhu, Yici Cai, Qiang Zhou, Bei Yu
Abstract
Energy efficiency has become the core issue of modern CPUs, and it is difficult for existing power models to balance speed, generality, and accuracy. This paper introduces McPAT-Calib, a microarchitecture power modeling framework, which combines McPAT with machine learning (ML) calibration methods. McPAT-Calib can quickly and accurately estimate the power of different benchmarks running on different CPU configurations, and provide an effective evaluation tool for the design of modern CPUs. First, McPAT-7nm is introduced to support the analytical power modeling for the 7nm technology node. Then, a wide range of modeling features are identified, and automatic feature selection and advanced regression methods are used to calibrate the McPAT-7nm modeling results, which greatly improves the generality and accuracy. Moreover, a sampling algorithm based on active learning (AL) is leveraged to effectively reduce the labeling cost. We use up to 15 configurations of 7nm RISC-V Berkeley Out-of-Order Machine (BOOM) along with 80 benchmarks to extensively evaluate the proposed framework. Compared with state-of-the-art microarchitecture power models, McPAT-Calib can reduce the mean absolute percentage error (MAPE) of shuffle-split cross-validation by 5.95%. More importantly, the MAPE is reduced by 6.14% and 3.64% for the evaluations of unknown CPU configurations and benchmarks, respectively. The AL sampling algorithm can reduce the demand of labeled samples by 50 %, while the accuracy loss is only 0.44 %.