APOLLO: An Automated Power Modeling Framework for Runtime Power Introspection in High-Volume Commercial Microprocessors
Zhiyao Xie, Xiaoqing Xu, Matt Walker, Joshua Knebel, Kumaraguru Palaniswamy, Nicolas Hébert, Jiang Hu, Huanrui Yang, Yiran Chen, Shidhartha Das · 2021
Accurate power modeling is crucial for energy-efficient CPU design and runtime management. An ideal power modeling framework needs to be accurate yet fast, achieve high temporal resolution (ideally cycle-accurate) yet with low runtime computational overheads, and easily extensible to diverse designs through automation. Simultaneously satisfying such conflicting objectives is challenging and largely unattained despite significant prior research.