# Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques

arXiv: https://arxiv.org/abs/2309.16733 · HTML: https://mlsanity.com/paper/2309.16733/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 108 references that are in the map.
- First posted on arXiv 27 Sep 2023.

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

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://mlsanity.com/paper/1612.07119/) (2016)
- [FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation](https://mlsanity.com/paper/1912.00941/) (2019)
- [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)
- [When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies](https://mlsanity.com/paper/1909.04697/) (2019)
- [FAT: Training Neural Networks for Reliable Inference Under Hardware Faults](https://mlsanity.com/paper/2011.05873/) (2020)
- [Making Convolutions Resilient via Algorithm-Based Error Detection Techniques](https://mlsanity.com/paper/2006.04984/) (2020)
- [FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions](https://mlsanity.com/paper/2112.13544/) (2021)
- [DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators](https://mlsanity.com/paper/2303.08226/) (2023)
- [In-Place Zero-Space Memory Protection for CNN](https://mlsanity.com/paper/1910.14479/) (2019)
- [Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs](https://mlsanity.com/paper/2104.09455/) (2021)
- [Fast and Accurate Error Simulation for CNNs against Soft Errors](https://mlsanity.com/paper/2206.02051/) (2022)
- [Exposing Reliability Degradation and Mitigation in Approximate DNNs under Permanent Faults](https://mlsanity.com/paper/2301.07484/) (2023)

## Built on by (most cited first, 4 of 4)

- [DeepVigor+: Scalable and Accurate Semi-Analytical Fault Resilience Analysis for Deep Neural Network](https://mlsanity.com/paper/2410.15742/) (2024)
- [Efficient Triple Modular Redundancy for Reliability Enhancement of DNNs Using Explainable AI](https://mlsanity.com/paper/2507.08829/) (2025)
- [CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks](https://mlsanity.com/paper/2607.15753/) (2026)
- [TreeFI: Value-Aware Statistical Fault Injection for Deep Neural Networks](https://mlsanity.com/paper/2609.04912/) (2026)

## Abstract (arXiv)

Machine Learning (ML) is currently being exploited in numerous applications being one of the most effective Artificial Intelligence (AI) technologies, used in diverse fields, such as vision, autonomous systems, and alike. The trend motivated a significant amount of contributions to the analysis and design of ML applications against faults affecting the underlying hardware. The authors investigate the existing body of knowledge on Deep Learning (among ML techniques) resilience against hardware faults systematically through a thoughtful review in which the strengths and weaknesses of this literature stream are presented clearly and then future avenues of research are set out. The review is based on 220 scientific articles published between January 2019 and March 2024. The authors adopt a classifying framework to interpret and highlight research similarities and peculiarities, based on several parameters, starting from the main scope of the work, the adopted fault and error models, to their reproducibility. This framework allows for a comparison of the different solutions and the identification of possible synergies. Furthermore, suggestions concerning the future direction of research are proposed in the form of open challenges to be addressed.