标题： AutoPower：通过功率组解耦的自动少样本架构级功率建模 显示英文标题

标题： AutoPower: Automated Few-Shot Architecture-Level Power Modeling by Power Group Decoupling

摘要： 功率效率是现代CPU设计中的关键设计目标。 架构师需要一个快速且准确的架构级功率评估工具来进行早期阶段的功率估计。 然而，传统的分析型架构级功率模型不准确。 最近提出的基于机器学习（ML）的架构级功率模型需要来自已知配置的足够数据进行训练，这使其不现实。 在本工作中，我们提出了AutoPower，旨在使用有限的已知设计配置实现完全自动化的架构级功率建模。 我们有两个关键观察：（1）时钟和SRAM主导了处理器的功耗，（2）时钟和SRAM的功耗与架构级别可获得的结构信息相关。 基于这两个观察，我们在AutoPower中提出了功率组解耦。 首先， AutoPower在不同功率组之间进行解耦，为每个组建立单独的功率模型。 其次，AutoPower通过进一步将模型解耦为每个功率组内的多个子模型来设计功率模型。 在我们的实验中， 即使仅使用两个已知配置进行训练，AutoPower也能达到低至4.36%的平均绝对百分比误差（MAPE）和高至$R^2$的0.96。 与McPAT-Calib，这一代表性的基于机器学习的功率模型相比，MAPE降低了5%，$R^2$高出0.09。 显示英文摘要

摘要： Power efficiency is a critical design objective in modern CPU design. Architects need a fast yet accurate architecture-level power evaluation tool to perform early-stage power estimation. However, traditional analytical architecture-level power models are inaccurate. The recently proposed machine learning (ML)-based architecture-level power model requires sufficient data from known configurations for training, making it unrealistic. In this work, we propose AutoPower targeting fully automated architecture-level power modeling with limited known design configurations. We have two key observations: (1) The clock and SRAM dominate the power consumption of the processor, and (2) The clock and SRAM power correlate with structural information available at the architecture level. Based on these two observations, we propose the power group decoupling in AutoPower. First, AutoPower decouples across power groups to build individual power models for each group. Second, AutoPower designs power models by further decoupling the model into multiple sub-models within each power group. In our experiments, AutoPower can achieve a low mean absolute percentage error (MAPE) of 4.36\% and a high $R^2$ of 0.96 even with only two known configurations for training. This is 5\% lower in MAPE and 0.09 higher in $R^2$ compared with McPAT-Calib, the representative ML-based power model.