# GPU Temperature Simulation-Based Testing for In-Vehicle Deep Learning Frameworks

arXiv: https://arxiv.org/abs/2509.15815 · HTML: https://mlsanity.com/paper/2509.15815/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 64 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Deep Learning with Limited Numerical Precision](https://mlsanity.com/paper/1502.02551/) (2015)
- [Taxonomy of Real Faults in Deep Learning Systems](https://mlsanity.com/paper/1910.11015/) (2019)
- [A Comprehensive Study on Deep Learning Bug Characteristics](https://mlsanity.com/paper/1906.01388/) (2019)
- [Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies](https://mlsanity.com/paper/2006.06091/) (2020)
- [Repairing Deep Neural Networks: Fix Patterns and Challenges](https://mlsanity.com/paper/2005.00972/) (2020)
- [Free Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source](https://mlsanity.com/paper/2201.06589/) (2022)
- [Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing](https://mlsanity.com/paper/2204.08734/) (2022)
- [Fuzzing Deep-Learning Libraries via Automated Relational API Inference](https://mlsanity.com/paper/2207.05531/) (2022)
- [Impact of Thermal Throttling on Long-Term Visual Inference in a CPU-based Edge Device](https://mlsanity.com/paper/2010.06291/) (2020)
- [A Survey and Measurement Study of GPU DVFS on Energy Conservation](https://mlsanity.com/paper/1610.01784/) (2016)
- [COMET: Coverage-guided Model Generation For Deep Learning Library Testing](https://mlsanity.com/paper/2208.01508/) (2022)
- [Thermal Analysis for NVIDIA GTX480 Fermi GPU Architecture](https://mlsanity.com/paper/2403.16239/) (2024)

## Abstract (arXiv)

Deep learning models play a vital role in autonomous driving systems, supporting critical functions such as environmental perception. To accelerate model inference, these deep learning models' deployment relies on automotive deep learning frameworks, for example, PaddleInference in Apollo and TensorRT in AutoWare. However, unlike deploying deep learning models on the cloud, vehicular environments experience extreme ambient temperatures varying from -40degC to 50degC, significantly impacting GPU temperature. Additionally, heats generated when computing further lead to the GPU temperature increase. These temperature fluctuations lead to dynamic GPU frequency adjustments through mechanisms such as DVFS. However, automotive deep learning frameworks are designed without considering the impact of temperature-induced frequency variations. When deployed on temperature-varying GPUs, these frameworks suffer critical quality issues: compute-intensive operators face delays or errors, high/mixed-precision operators suffer from precision errors, and time-series operators suffer from synchronization issues. The above quality issues cannot be detected by existing deep learning framework testing methods because they ignore temperature's effect on the deep learning framework quality. To bridge this gap, we propose ThermalGuardian, the first automotive deep learning framework testing method under temperature-varying environments. Specifically, ThermalGuardian generates test input models using model mutation rules targeting temperature-sensitive operators, simulates GPU temperature fluctuations based on Newton's law of cooling, and controls GPU frequency based on real-time GPU temperature.