FirePower: Towards a Foundation with Generalizable Knowledge for Architecture-Level Power Modeling
Qijun Zhang, Mengming Li, Yao Lu, Zhiyao Xie · 2025
Power efficiency is a critical design objective in modern processor design. A high-fidelity architecture-level power modeling method is greatly needed by CPU architects for guiding early optimizations. However, traditional architecture-level power models can not meet the accuracy requirement, largely due to the discrepancy between the power model and actual design implementation. While some machine learning (ML)-based architecture-level power modeling methods have been proposed in recent years, the data-hungry ML model training process requires sufficient similar known designs, which are unrealistic in many development scenarios.