# Adaptive Soft Error Protection for Neural Network Processing

arXiv: https://arxiv.org/abs/2407.19664 · HTML: https://mlsanity.com/paper/2407.19664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 63 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [A Survey on Multimodal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.12320/) (2023)
- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks](https://mlsanity.com/paper/2003.12203/) (2020)
- [DAC-SDC Low Power Object Detection Challenge for UAV Applications](https://mlsanity.com/paper/1809.00110/) (2018)
- [Soft Error Reliability Analysis of Vision Transformers](https://mlsanity.com/paper/2302.10468/) (2023)
- [Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs](https://mlsanity.com/paper/2104.09455/) (2021)
- [Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators](https://mlsanity.com/paper/2401.09509/) (2024)
- [R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors](https://mlsanity.com/paper/2107.03096/) (2021)
- [Winograd Convolution: A Perspective from Fault Tolerance](https://mlsanity.com/paper/2202.08675/) (2022)
- [Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs](https://mlsanity.com/paper/2305.01024/) (2023)
- [MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing](https://mlsanity.com/paper/2306.11758/) (2023)
- [Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning](https://mlsanity.com/paper/2405.10658/) (2024)
- [Machine Learning Clustering Techniques for Selective Mitigation of Critical Design Features](https://mlsanity.com/paper/2008.13664/) (2020)
- [FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design](https://mlsanity.com/paper/2305.19153/) (2023)
- [Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance](https://mlsanity.com/paper/2308.08230/) (2023)
- [On the Prediction of Hardware Security Properties of HLS Designs Using Graph Neural Networks](https://mlsanity.com/paper/2312.07594/) (2023)

## Abstract (arXiv)

Previous research on selective protection for neural network components typically exploits only static vulnerability differences. Although these methods improve upon classical modular redundancy, they still incur substantial overhead for neural network workloads that are both memory-intensive and compute-intensive. In this work, we observe that neural network vulnerability is also input-dependent and varies dynamically at runtime. With this observation, we propose an adaptive, vulnerability-aware fault tolerance framework. At its core, a lightweight graph neural network (GNN) model dynamically predicts soft error vulnerabilities across inputs and neural network components, enabling real-time adaptation of fault tolerance policies. This design offers a complementary and more efficient protection scheme compared to traditional approaches. Experimental results demonstrate that the GNN predictor achieves over 95% accuracy in identifying critical inputs and components. Moreover, our adaptive scheme reduces computational overhead by an average of 42.12% while preserving model accuracy, significantly outperforming static selective protection methods.