Loading
Life sciences · Preprint
arXiv · September 4, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unreviewed engineering preprint demonstrating that an empirically calibrated DVFS scheduler eliminates thermal throttling on a single passively cooled edge device (Raspberry Pi 5) during sustained neural network inference. The work is a feasibility and optimization study with no applicability to clinical practice or medical research and remains subject to peer review.
Single-platform empirical optimization study with ablations and internal baselines. Raspberry Pi 5 (passively cooled SoC) running YOLOv8n neural network inference workload. Intervention: Empirically calibrated DVFS scheduler with time-domain guards, absolute temperature bounds, and derivative-based spike safeguards. Compared with: Temperature-only reactive baseline and actively cooled reference system.
Scheduler eliminates all observed thermal throttling events during sustained 30-minute workloads on Raspberry Pi 5 running YOLOv8n Outperforms temperature-only reactive baseline by 6.8% higher frame rate (Cohen's d = 8.73) Consumes 1.9% less energy per frame than baseline
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Single-platform engineering optimization study with no clinical or patient outcomes; demonstrates feasibility of DVFS scheduling on one edge device but lacks generalizability, peer review, and clinical relevance.
As stated by the source record.
Quoted from the source exactly as published.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.