# Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective

arXiv: https://arxiv.org/abs/2412.03630 · HTML: https://mlsanity.com/paper/2412.03630/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation](https://mlsanity.com/paper/1912.00941/) (2019)
- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [Improving aircraft performance using machine learning: a review](https://mlsanity.com/paper/2210.11481/) (2022)
- [When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies](https://mlsanity.com/paper/1909.04697/) (2019)
- [FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions](https://mlsanity.com/paper/2112.13544/) (2021)
- [HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Driving](https://mlsanity.com/paper/2411.17530/) (2024)
- [Retrospective: Flipping Bits in Memory Without Accessing Them: An Experimental Study of DRAM Disturbance Errors](https://mlsanity.com/paper/2306.16093/) (2023)

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

- [Hyperspectral Sensors and Autonomous Driving: Technologies, Limitations, and Opportunities](https://mlsanity.com/paper/2508.19905/) (2025)
- [Multi-Scale Spectral Attention Module-based Hyperspectral Segmentation in Autonomous Driving Scenarios](https://mlsanity.com/paper/2506.18682/) (2025)
- [DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation](https://mlsanity.com/paper/2504.05122/) (2025)
- [Optimization of DNN-based HSI Segmentation FPGA-based SoC for ADS: A Practical Approach](https://mlsanity.com/paper/2507.16556/) (2025)
- [Reliable Explainability of Deep Learning Spatial-Spectral Classifiers for Improved Semantic Segmentation in Autonomous Driving](https://mlsanity.com/paper/2502.14416/) (2025)
- [Challenges in Hyperspectral Imaging for Autonomous Driving: The HSI-Drive Case](https://mlsanity.com/paper/2603.25510/) (2026)
- [Lazy Arithmetic using Systolic Arrays for Closing the Verification Gap on Embedded Systems](https://mlsanity.com/paper/2607.15328/) (2026)
- [TreeFI: Value-Aware Statistical Fault Injection for Deep Neural Networks](https://mlsanity.com/paper/2609.04912/) (2026)

## Abstract (arXiv)

As the deployment of artifical intelligence (AI) algorithms at edge devices becomes increasingly prevalent, enhancing the robustness and reliability of autonomous AI-based perception and decision systems is becoming as relevant as precision and performance, especially in applications areas considered safety-critical such as autonomous driving and aerospace. This paper delves into the robustness assessment in embedded Deep Neural Networks (DNNs), particularly focusing on the impact of parameter perturbations produced by single event upsets (SEUs) on convolutional neural networks (CNN) for image semantic segmentation. By scrutinizing the layer-by-layer and bit-by-bit sensitivity of various encoder-decoder models to soft errors, this study thoroughly investigates the vulnerability of segmentation DNNs to SEUs and evaluates the consequences of techniques like model pruning and parameter quantization on the robustness of compressed models aimed at embedded implementations. The findings offer valuable insights into the mechanisms underlying SEU-induced failures that allow for evaluating the robustness of DNNs once trained in advance. Moreover, based on the collected data, we propose a set of practical lightweight error mitigation techniques with no memory or computational cost suitable for resource-constrained deployments. The code used to perform the fault injection (FI) campaign is available at https://github.com/jonGuti13/TensorFI2 , while the code to implement proposed techniques is available at https://github.com/jonGuti13/parameterProtection .