APOLLO: An Automated Power Modeling Framework for Runtime Power Introspection in High-Volume Commercial Microprocessors
Zhiyao Xie, Xiaoqing Xu, M. Walker, Joshua Knebel, Kumaraguru Palaniswamy, Nicolas Hébert, Jiang Hu, Huanrui Yang, Yiran Chen, Shidhartha Das
Abstract
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.
Topics & Concepts
Computer scienceElectronic design automationPower (physics)AutomationVolume (thermodynamics)Embedded systemPower managementDistributed computingEngineeringQuantum mechanicsMechanical engineeringPhysicsParallel Computing and Optimization TechniquesLow-power high-performance VLSI designEmbedded Systems Design Techniques