# Fault-Tolerant Deep Learning: A Hierarchical Perspective

arXiv: https://arxiv.org/abs/2204.01942 · HTML: https://mlsanity.com/paper/2204.01942/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 87 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 15 of 15)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [RobustART: Benchmarking Robustness on Architecture Design and Training Techniques](https://mlsanity.com/paper/2109.05211/) (2021)
- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [Analyzing and Mitigating the Impact of Permanent Faults on a Systolic Array Based Neural Network Accelerator](https://mlsanity.com/paper/1802.04657/) (2018)
- [FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks](https://mlsanity.com/paper/2003.12203/) (2020)
- [ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators](https://mlsanity.com/paper/1802.03806/) (2018)
- [On the Resilience of RTL NN Accelerators: Fault Characterization and Mitigation](https://mlsanity.com/paper/1806.09679/) (2018)
- [Training Modern Deep Neural Networks for Memory-Fault Robustness](https://mlsanity.com/paper/1911.10287/) (2019)
- [Winograd Convolution: A Perspective from Fault Tolerance](https://mlsanity.com/paper/2202.08675/) (2022)
- [HyCA: A Hybrid Computing Architecture for Fault Tolerant Deep Learning](https://mlsanity.com/paper/2106.04772/) (2021)
- [A Study of Deep Learning Robustness Against Computation Failures](https://mlsanity.com/paper/1704.05396/) (2017)
- [A Survey on Impact of Transient Faults on BNN Inference Accelerators](https://mlsanity.com/paper/2004.05915/) (2020)

## Built on by (most cited first, 5 of 5)

- [MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing](https://mlsanity.com/paper/2306.11758/) (2023)
- [Statistical Modeling of Soft Error Influence on Neural Networks](https://mlsanity.com/paper/2210.05876/) (2022)
- [Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance](https://mlsanity.com/paper/2308.08230/) (2023)
- [Cross-Layer Optimization for Fault-Tolerant Deep Learning](https://mlsanity.com/paper/2312.13754/) (2023)
- [Analysis of LLM Vulnerability to GPU Soft Errors: An Instruction-Level Fault Injection Study](https://mlsanity.com/paper/2601.19912/) (2025)

## Abstract (arXiv)

With the rapid advancements of deep learning in the past decade, it can be foreseen that deep learning will be continuously deployed in more and more safety-critical applications such as autonomous driving and robotics. In this context, reliability turns out to be critical to the deployment of deep learning in these applications and gradually becomes a first-class citizen among the major design metrics like performance and energy efficiency. Nevertheless, the back-box deep learning models combined with the diverse underlying hardware faults make resilient deep learning extremely challenging. In this special session, we conduct a comprehensive survey of fault-tolerant deep learning design approaches with a hierarchical perspective and investigate these approaches from model layer, architecture layer, circuit layer, and cross layer respectively.