Abstract: Power efficiency has become a nonneglected issue of modern CPUs. Therefore, accurate and robust power models are highly demanded in academia and industry. However, it is hard for existing power models to balance modeling speed, generality, and accuracy well. This article introduces McPAT-Calib, a microarchitecture power modeling framework, which combines McPAT with machine learning (ML) calibration and active learning (AL) sampling. McPAT-Calib can quickly and accurately estimate the power of different benchmarks executed on different CPU configurations, and provide an effective evaluation tool for the early design stage. First, McPAT-7nm is introduced to support the preliminary analytical power modeling for the 7-nm technology node. Then, a wide range of modeling features are identified, and automatic feature selection and advanced nonlinear regression are used to calibrate the McPAT-7nm modeling results, greatly improving the accuracy. Moreover, a novel AL approach termed power greedy sampling (PowerGS) embedded with domain knowledge is leveraged to reduce the modeling cost effectively. We use up to 15 configurations of the RISC-V Berkeley out-of-order machine (BOOM) along with 80 benchmarks, targeting 7-nm technology, to extensively evaluate McPAT-Calib. Compared with state-of-the-art (SOTA) microarchitecture power models, McPAT-Calib can reduce the mean absolute percentage error (MAPE) under different cross-validation (CV) strategies by 3.64%–6.14% (absolute reduction). Meanwhile, PowerGS is superior to the existing AL approaches, which can significantly reduce the demand for labeled samples to speed up model construction. The effectiveness of the overall modeling and estimation flow with AL sampling has also been verified.