# When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies

arXiv: https://arxiv.org/abs/1909.04697 · HTML: https://mlsanity.com/paper/1909.04697/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 15 of 15)

- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search](https://mlsanity.com/paper/2107.06871/) (2021)
- [SWIM: Selective Write-Verify for Computing-in-Memory Neural Accelerators](https://mlsanity.com/paper/2202.08395/) (2022)
- [Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?](https://mlsanity.com/paper/2207.07626/) (2022)
- [On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators](https://mlsanity.com/paper/2306.06923/) (2023)
- [Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators through Training with Right-Censored Gaussian Noise](https://mlsanity.com/paper/2307.15853/) (2023)
- [Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective](https://mlsanity.com/paper/2412.03630/) (2024)
- [Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques](https://mlsanity.com/paper/2309.16733/) (2023)
- [Compute-in-Memory based Neural Network Accelerators for Safety-Critical Systems: Worst-Case Scenarios and Protections](https://mlsanity.com/paper/2312.06137/) (2023)
- [LLMSat: A Large Language Model-Based Goal-Oriented Agent for Autonomous Space Exploration](https://mlsanity.com/paper/2405.01392/) (2024)
- [DeepVigor+: Scalable and Accurate Semi-Analytical Fault Resilience Analysis for Deep Neural Network](https://mlsanity.com/paper/2410.15742/) (2024)
- [PhD Thesis Summary: Methods for Reliability Assessment and Enhancement of Deep Neural Network Hardware Accelerators](https://mlsanity.com/paper/2603.08724/) (2026)
- [U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural Accelerators](https://mlsanity.com/paper/2401.05357/) (2023)
- [Efficient Triple Modular Redundancy for Reliability Enhancement of DNNs Using Explainable AI](https://mlsanity.com/paper/2507.08829/) (2025)
- [SPINE: A Fault Injection Profiler for Quantized Neural Networks under Accumulated Faults](https://mlsanity.com/paper/2606.19526/) (2026)

## Abstract (arXiv)

Deep Neural Network has proved its potential in various perception tasks and hence become an appealing option for interpretation and data processing in security sensitive systems. However, security-sensitive systems demand not only high perception performance, but also design robustness under various circumstances. Unlike prior works that study network robustness from software level, we investigate from hardware perspective about the impact of Single Event Upset (SEU) induced parameter perturbation (SIPP) on neural networks. We systematically define the fault models of SEU and then provide the definition of sensitivity to SIPP as the robustness measure for the network. We are then able to analytically explore the weakness of a network and summarize the key findings for the impact of SIPP on different types of bits in a floating point parameter, layer-wise robustness within the same network and impact of network depth. Based on those findings, we propose two remedy solutions to protect DNNs from SIPPs, which can mitigate accuracy degradation from 28% to 0.27% for ResNet with merely 0.24-bit SRAM area overhead per parameter.