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

arXiv: https://arxiv.org/abs/2010.06291 · HTML: https://mlsanity.com/paper/2010.06291/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Play It Cool: Dynamic Shifting Prevents Thermal Throttling](https://mlsanity.com/paper/2206.10849/) (2022)
- [MTD: Multi-Timestep Detector for Delayed Streaming Perception](https://mlsanity.com/paper/2309.06742/) (2023)
- [Leveraging Neural Graph Compilers in Machine Learning Research for Edge-Cloud Systems](https://mlsanity.com/paper/2504.20198/) (2025)
- [Introducing Instruction-Accurate Simulators for Performance Estimation of Autotuning Workloads](https://mlsanity.com/paper/2505.13357/) (2025)
- [GPU Temperature Simulation-Based Testing for In-Vehicle Deep Learning Frameworks](https://mlsanity.com/paper/2509.15815/) (2025)
- [T3C: Test-Time Tensor Compression with Consistency Guarantees](https://mlsanity.com/paper/2601.01299/) (2026)
- [Beyond Benchmarks: Continuous Edge Inference for Fine-Grained Roadside Perception](https://mlsanity.com/paper/2606.17241/) (2026)
- [Transferable Latency Prediction for Fast LLM Screening on Heterogeneous Edge Devices](https://mlsanity.com/paper/2607.21602/) (2026)

## Abstract (arXiv)

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 degC to 36 degC. 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.