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.
翻译:边缘视觉发酵的许多应用情景,例如机器人或环境监测,最终需要长期连续运行。在这样的时期内,处理器温度对于保持规定的框架速率起着关键作用。特别是,卷发神经网络(CNNs)的重计算载荷可能会导致热抽动,从而在几秒钟内导致性能退化。在本文中,我们报告和分析在4个软件框架和2个操作系统上运行5个CNN模型导致80个不同案例的长期性能,没有和有积极冷却。这一全面研究是在一个低成本的边缘平台,即Raspberry Pi 4B(RPi4B),在稳定的室内条件下进行的。结果显示,基于歇斯底里活性冷能的计算负荷防止了所有情况下的热抽动,从而将输电量提高到大约90%,而没有冷却。有趣的是,在主动冷却过程中,风扇的使用范围从33%到65%不等。鉴于风扇对系统电力消耗的影响,因此,我们不得不强调适当选择CNN模型和软件的精度,在稳定的内部条件下,从22次的温度测试中,从进行积极温度测试,从进行。