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APOLLO: An Automated Power Modeling Framework For Runtime Power Introspection In High-Volume Commercial Microprocessors


Zhiyao Xie

The Hong Kong University of Science and Technology

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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. In this talk, I will introduce our work APOLLO with multiple key attributes. First, it supports fast and accurate design-time power model simulation, handling millions-of-cycles benchmarks in minutes with an emulator. Second, it incorporates an unprecedented low-cost runtime on-chip power meter in CPU RTL for per-cycle power tracing. Third, the development process of this method is fully automated and applies to any given design solution. This method has been validated on high-volume commercial microprocessors Neoverse N1 and Cortex-A77.


About DAWN - "The best of EDA research in 2021" invites the researchers to give talks about their papers that received best paper awards from EDA-related journals (e.g., IEEE TCAD) and conferences including MICRO, DAC, ICCAD, DATE, ASP-DAC, and ESWEEK). This is a two-day webinar including four, 20-minute talks (a 15-minute presentation with a 5-minute Q&A).