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arXiv Machine Learning By Aayush Marasini, Zhaoxian Zhou

Sustainable Edge Vision via Empirically Calibrated DVFS: Eliminating Thermal Throttling on Passively Cooled Hardware

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The paper presents an empirically calibrated, state‑aware Dynamic Voltage and Frequency Scaling (DVFS) scheduler that eliminates thermal throttling on a passively cooled Raspberry Pi 5 during sustained YOLOv8n inference. By using time‑domain guards, absolute temperature bounds, and derivative triggers, the scheduler outperforms a temperature‑only baseline with a 6.8% higher frame rate and 1.9% less energy per frame, and it surpasses an actively cooled reference in energy efficiency. The study also identifies that the passive operating envelope closes at ambient temperatures above 27 °C, where nonlinear leakage undermines DVFS control, and demonstrates that correct scheduling can make mechanical cooling unnecessary within the mapped envelope.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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