Publication | Open Access
APOLLO: An Automated Power Modeling Framework for Runtime Power Introspection in High-Volume Commercial Microprocessors
46
Citations
71
References
2021
Year
Unknown Venue
EngineeringEnergy EfficiencyPower Optimization (Eda)Computer ArchitectureSystems EngineeringModeling And SimulationHigh-volume Commercial MicroprocessorsAccurate Power ModelingParallel ComputingPower-aware DesignRuntime ManagementPower SystemsPower ManagementPower-aware SoftwarePower-aware ComputingComputer EngineeringComputer ScienceSmart GridIdeal PowerRuntime Power Introspection
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.
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