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Sustainable Edge Vision via Empirically Calibrated DVFS: Eliminating Thermal Throttling on Passively Cooled Hardware — arXiv:2609.04705

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.04705
License (as archived): CC0-1.0
Archived: 2026-10-16T22:42:19.443Z
SHA-256 of the archived text: 93abfa26ef68989ce56d7be8475444d77e5313fd29d68e4b1ee61eab8489522b
Stable URL of this page: https://metraipolis.com/documento/dc_bbbda859feab

How to cite

Sustainable Edge Vision via Empirically Calibrated DVFS: Eliminating Thermal Throttling on Passively Cooled Hardware — arXiv:2609.04705 — arXiv (en), https://arxiv.org/abs/2609.04705 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bbbda859feab (Metraipolis archive dc_bbbda859feab, sha256 93abfa26ef68989ce56d7be8475444d77e5313fd29d68e4b1ee61eab8489522b)

BibTeX

@misc{dcbbbda859feab,
  title = {Sustainable Edge Vision via Empirically Calibrated DVFS: Eliminating Thermal Throttling on Passively Cooled Hardware — arXiv:2609.04705},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.04705},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bbbda859feab; sha256 93abfa26ef68989ce56d7be8475444d77e5313fd29d68e4b1ee61eab8489522b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.04705. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Passive cooling eliminates the energy overhead and mechanical failure modes of fans, making it attractive for edge deployment, yet sustained Deep Neural Network (DNN) inference on passively cooled edge Systems-on-Chip (SoCs) is bottlenecked by thermal throttling. To address this, we propose an empirically calibrated, state-aware Dynamic Voltage and Frequency Scaling (DVFS) scheduler. Unlike heuristic-driven controllers, our methodology utilizes time-domain guards and absolute temperature bounds, with derivative triggers acting as safeguards against sharp thermal spikes. Evaluated on a passively cooled Raspberry Pi 5 running YOLOv8n, our scheduler eliminates all observed thermal throttling events during sustained 30-minute workloads. It outperforms a temperature-only reactive baseline by achieving a 6.8% higher frame rate (Cohen's d = 8.73) while consuming 1.9% less energy per frame. Furthermore, our optimized passive scheduling surpasses an actively cooled reference system in energy efficiency (Joules/frame), though active cooling remains superior for raw throughput. Through isolated ablations, we show that the dwell guard is necessary for run-to-run reproducibility. Finally, exploratory boundary probes indicate that the passive operating envelope closes at ambient temperatures ($\ge 27^\circ$C) where nonlinear leakage defeats DVFS-based control. These results indicate that, within the mapped envelope, correct scheduling can make mechanical cooling unnecessary for sustained edge inference on this platform.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-07 in cs.AR).
Categorie arXiv: Hardware Architecture (cs.AR) ; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.04705 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T22:26:48.909Z dalla pagina https://arxiv.org/catchup/cs.AR/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).