提出新方法精准估算异构手机设备的功耗，提升联邦学习能效。
A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices
- 通过轨到集群映射技术获取电压信息，实现可复现的功耗估计。
- 分析模型误差低于10%，远优于近似模型最高959%的误差。
- 在保持模型精度前提下，能耗降低40%，适合移动端能效优化研究。
由于对CPU电压域访问受限，异构ARM设备上的CPU功耗估计极具挑战。现有能量感知联邦学习框架多依赖简化近似功耗模型，而非更准确的解析式CMOS模型。为此，本文提出一种可复现的CPU功耗估计方法，结合轨到集群映射技术，以恢复集群级供电电压。我们在两款商用Android设备上评估该方法，结果表明：解析模型预测误差低于10%，而近似模型误差高达959%。在AnycostFL框架下，解析模型在保持80%模型精度的同时，能耗仅为近似模型的1.4倍，即节能40%。这说明近似模型会严重误估计算能耗，导致次优决策。本工作无需额外硬件或外部测量工具，即可在异构多集群ARM移动SoC上应用解析式功耗模型。
原文摘要 · Abstract (English)
Estimating CPU power on heterogeneous ARM-based commodity devices is challenging due to limited access to CPU's voltage domains. As a result, state-of-the-art energy-aware Federated Learning (FL) frameworks typically rely on simplified approximate power models to estimate computation energy, rather than the more accurate analytical CMOS-based model. To bridge this gap, we propose a reproducible CPU power estimation methodology combined with a rail-to-cluster mapping technique to retrieve cluster-level supply voltage. We evaluate our approach on two commodity Android devices and show that the analytical model predicts CPU power with errors below 10%, whereas the approximate model incurs errors of up to 959%. Using AnycostFL, a state-of-the-art energy-aware FL framework, we show that the analytical model achieves the same 80% model accuracy while consuming 1.4x less energy than the approximate model. These results highlight that approximate models can severely misestimate computation energy and lead to suboptimal decisions. This work facilitates the use of analytical CPU power models on heterogeneous multi-cluster ARM-based mobile SoCs without additional hardware support or external power measurement tools.
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