Passively cooled edge hardware eliminates fan failure modes and standby power, but the tradeoff has always been thermal throttling under sustained inference load. This paper maps the control problem precisely: a state-aware DVFS scheduler with absolute temperature bounds and derivative triggers that catch sharp thermal spikes before the governor steps in. On a passively cooled Raspberry Pi 5 running YOLOv8n continuously for 30 minutes, the calibrated scheduler eliminates all throttling events and beats a temperature-only reactive baseline by 6.8% frame rate (Cohen's d = 8.73) while consuming 1.9% less energy per frame. The open question of whether passive could beat active cooling on energy efficiency is now answered for this platform: yes, at the cost of raw throughput.
The dwell guard is the structural contribution. Without it, run-to-run reproducibility collapses because short-cycle frequency changes destabilize thermal state. The scheduler also maps the operating envelope boundary: at ambient temperatures above 27C, nonlinear leakage defeats DVFS-based control entirely. That boundary is a product spec for every passively cooled edge device running inference in summer field conditions or enclosures with poor convection, and until now it was typically discovered during board-bring-up rather than at design time.
The code and datasets are open source on GitHub. The meaningful takeaway for embedded teams is that fan-less AI edge products are viable across a wider operating envelope than thermal derate curves suggest, but only with a calibrated governor -- not a generic SoC thermal policy. Teams building inference appliances for outdoor, industrial, or medical enclosures where mechanical cooling is impractical now have a validated scheduler pattern and a clear constraint: size the thermal mass for the 27C ambient ceiling, or accept that the operating envelope closes.