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09 April 2025
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Last updated on
09 April 2025 |
S. Mazzola, G. Ara, T. Benz, B. Forsberg, T. Cucinotta, Luca Benini. "Data-Driven Power Modeling and Monitoring via Hardware Performance Counter Tracking," (to appear in) Elsevier Journal of Systems Architecture (JSA), 2025.
Energy-centric design is paramount in the current embedded computing era: use cases require increasingly high performance at an affordable power budget, often under real-time constraints. Hardware heterogeneity and parallelism help address the efficiency challenge, but greatly complicate online power consumption assessments, which are essential for dynamic hardware and software stack adaptations. We introduce a novel architecture-agnostic power modeling methodology with state-of-the-art accuracy, low overhead, and high responsiveness. Our methodology identifies the Performance Monitoring Counters (PMCs) with the highest linear correlation to the power consumption of each hardware sub-system, for each Dynamic Voltage and Frequency Scaling (DVFS) state. The individual, simple models are composed into a complete model that effectively describes the power consumption of the whole system, achieving high accuracy and low overhead. Our evaluation reports an average estimation error of 7.5 % for power consumption and 1.3 % for energy. We integrate these models in the Linux kernel with Runmeter, an open-source, PMC-based monitoring framework. Runmeter manages PMC sampling and processing, enabling the execution of our power models at runtime. With a worst-case time overhead of only 0.7 %, Runmeter provides responsive and accurate power measurements directly in the kernel. This information can be employed for actuation policies in workload-aware Dynamic Power Management (DPM) and power-aware, closed-loop task scheduling.
Copyright by Elsevier.
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Last updated on
11 April 2025 |