  Many application scenarios of edge visual inference, e.g., robotics or
environmental monitoring, eventually require long periods of continuous
operation. In such periods, the processor temperature plays a critical role to
keep a prescribed frame rate. Particularly, the heavy computational load of
convolutional neural networks (CNNs) may lead to thermal throttling and hence
performance degradation in few seconds. In this paper, we report and analyze
the long-term performance of 80 different cases resulting from running 5 CNN
models on 4 software frameworks and 2 operating systems without and with active
cooling. This comprehensive study was conducted on a low-cost edge platform,
namely Raspberry Pi 4B (RPi4B), under stable indoor conditions. The results
show that hysteresis-based active cooling prevented thermal throttling in all
cases, thereby improving the throughput up to approximately 90% versus no
cooling. Interestingly, the range of fan usage during active cooling varied
from 33% to 65%. Given the impact of the fan on the power consumption of the
system as a whole, these results stress the importance of a suitable selection
of CNN model and software components. To assess the performance in outdoor
applications, we integrated an external temperature sensor with the RPi4B and
conducted a set of experiments with no active cooling in a wide interval of
ambient temperature, ranging from 22 {\deg}C to 36 {\deg}C. Variations up to
27.7% were measured with respect to the maximum throughput achieved in that
interval. This demonstrates that ambient temperature is a critical parameter in
case active cooling cannot be applied.
