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Article

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

1
École Nationale Supérieure d’Électrotechnique, d’Électronique, d’Informatique, d’Hydraulique et des Télécommunications, BP 7122, 31071 Toulouse, France
2
Instituto de Microelectrónica de Sevilla, Universidad de Sevilla-CSIC, 41092 Sevilla, Spain
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(12), 2106; https://doi.org/10.3390/electronics9122106
Received: 28 October 2020 / Revised: 25 November 2020 / Accepted: 3 December 2020 / Published: 10 December 2020
(This article belongs to the Section Computer Science & Engineering)
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 five CNN models on four software frameworks and two 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 °C to 36 °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. View Full-Text
Keywords: ambient conditions; convolutional neural networks; edge vision; long-term inference; thermal throttling; Raspberry Pi ambient conditions; convolutional neural networks; edge vision; long-term inference; thermal throttling; Raspberry Pi
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MDPI and ACS Style

Benoit-Cattin, T.; Velasco-Montero, D.; Fernández-Berni, J. Impact of Thermal Throttling on Long-Term Visual Inference in a CPU-Based Edge Device. Electronics 2020, 9, 2106. https://doi.org/10.3390/electronics9122106

AMA Style

Benoit-Cattin T, Velasco-Montero D, Fernández-Berni J. Impact of Thermal Throttling on Long-Term Visual Inference in a CPU-Based Edge Device. Electronics. 2020; 9(12):2106. https://doi.org/10.3390/electronics9122106

Chicago/Turabian Style

Benoit-Cattin, Théo, Delia Velasco-Montero, and Jorge Fernández-Berni. 2020. "Impact of Thermal Throttling on Long-Term Visual Inference in a CPU-Based Edge Device" Electronics 9, no. 12: 2106. https://doi.org/10.3390/electronics9122106

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