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Many application scenarios of edge visual inference, e.g., robotics orenvironmental monitoring, eventually require long periods of continuousoperation. In such periods, the processor temperature plays a critical role tokeep a prescribed frame rate. Particularly, the heavy computational load ofconvolutional neural networks (CNNs) may lead to thermal throttling and henceperformance degradation in few seconds. In this paper, we report and analyzethe long-term performance of 80 different cases resulting from running 5 CNNmodels on 4 software frameworks and 2 operating systems without and with activecooling. This comprehensive study was conducted on a low-cost edge platform,namely Raspberry Pi 4B (RPi4B), under stable indoor conditions. The resultsshow that hysteresis-based active cooling prevented thermal throttling in allcases, thereby improving the throughput up to approximately 90% versus nocooling. Interestingly, the range of fan usage during active cooling variedfrom 33% to 65%. Given the impact of the fan on the power consumption of thesystem as a whole, these results stress the importance of a suitable selectionof CNN model and software components. To assess the performance in outdoorapplications, we integrated an external temperature sensor with the RPi4B andconducted a set of experiments with no active cooling in a wide interval ofambient temperature, ranging from 22 {\deg}C to 36 {\deg}C. Variations up to27.7% were measured with respect to the maximum throughput achieved in thatinterval. This demonstrates that ambient temperature is a critical parameter incase active cooling cannot be applied.

Impact of Thermal Throttling on Long-Term Visual Inference in a CPU-based Edge Device