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.

中文翻译：

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McPAT-Calib：RISC-V BOOM 微架构功耗建模框架


功效已成为现代 CPU 不可忽视的问题。因此，学术界和工业界迫切需要准确、鲁棒的功率模型。然而，现有的功率模型很难很好地平衡建模速度、通用性和准确性。本文介绍了 McPAT-Calib，一种微架构功耗建模框架，它将 McPAT 与机器学习 (ML) 校准和主动学习 (AL) 采样相结合。 McPAT-Calib可以快速准确地估计在不同CPU配置上执行的不同基准测试的性能，并为早期设计阶段提供有效的评估工具。首先，引入McPAT-7nm来支持7nm技术节点的初步分析功率建模。然后，识别出广泛的建模特征，并使用自动特征选择和高级非线性回归来校准McPAT-7nm建模结果，大大提高了精度。此外，利用嵌入领域知识的称为幂贪婪采样（PowerGS）的新型 AL 方法来有效降低建模成本。我们使用多达 15 种 RISC-V Berkeley 乱序机 (BOOM) 配置以及 80 个针对 7 纳米技术的基准测试来广泛评估 McPAT-Calib。与最先进的（SOTA）微架构功耗模型相比，McPAT-Calib可以将不同交叉验证（CV）策略下的平均绝对百分比误差（MAPE）降低3.64%–6.14%（绝对降低）。同时，PowerGS优于现有的AL方法，可以显着减少对标记样本的需求，从而加快模型构建速度。 AL 采样的整体建模和估计流程的有效性也得到了验证。