# A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks

arXiv: https://arxiv.org/abs/2305.05750 · HTML: https://mlsanity.com/paper/2305.05750/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 203 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://mlsanity.com/paper/1612.07119/) (2016)
- [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://mlsanity.com/paper/1801.06601/) (2018)
- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation](https://mlsanity.com/paper/1912.00941/) (2019)
- [Machine learning for Internet of Things data analysis: A survey](https://mlsanity.com/paper/1802.06305/) (2018)
- [Analyzing and Mitigating the Impact of Permanent Faults on a Systolic Array Based Neural Network Accelerator](https://mlsanity.com/paper/1802.04657/) (2018)
- [TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications](https://mlsanity.com/paper/2004.01743/) (2020)
- [HarDNN: Feature Map Vulnerability Evaluation in CNNs](https://mlsanity.com/paper/2002.09786/) (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)
- [On the Resilience of RTL NN Accelerators: Fault Characterization and Mitigation](https://mlsanity.com/paper/1806.09679/) (2018)
- [In-Place Zero-Space Memory Protection for CNN](https://mlsanity.com/paper/1910.14479/) (2019)
- [Efficient Error-Tolerant Quantized Neural Network Accelerators](https://mlsanity.com/paper/1912.07394/) (2019)
- [Fast and Accurate Error Simulation for CNNs against Soft Errors](https://mlsanity.com/paper/2206.02051/) (2022)
- [Exploring Fault-Energy Trade-offs in Approximate DNN Hardware Accelerators](https://mlsanity.com/paper/2101.02860/) (2021)
- [HyCA: A Hybrid Computing Architecture for Fault Tolerant Deep Learning](https://mlsanity.com/paper/2106.04772/) (2021)
- [MILR: Mathematically Induced Layer Recovery for Plaintext Space Error Correction of CNNs](https://mlsanity.com/paper/2010.14687/) (2020)
- [Characterizing a Neutron-Induced Fault Model for Deep Neural Networks](https://mlsanity.com/paper/2211.13094/) (2022)
- [Reliability Assessment of Neural Networks in GPUs: A Framework For Permanent Faults Injections](https://mlsanity.com/paper/2205.12177/) (2022)
- [Evaluation of Xilinx Deep Learning Processing Unit under Neutron Irradiation](https://mlsanity.com/paper/2206.01981/) (2022)

## Built on by (most cited first, 23 of 23)

- [Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators](https://mlsanity.com/paper/2401.09509/) (2024)
- [Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective](https://mlsanity.com/paper/2412.03630/) (2024)
- [SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators](https://mlsanity.com/paper/2403.02946/) (2024)
- [Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning](https://mlsanity.com/paper/2405.10658/) (2024)
- [Designing DNNs for a trade-off between robustness and processing performance in embedded devices](https://mlsanity.com/paper/2412.03682/) (2024)
- [AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators](https://mlsanity.com/paper/2403.02936/) (2024)
- [DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation](https://mlsanity.com/paper/2504.05122/) (2025)
- [Effective and Memory-Efficient Alternatives to ECC for Reliable Large-Scale DNNs](https://mlsanity.com/paper/2605.07417/) (2026)
- [ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs](https://mlsanity.com/paper/2406.06313/) (2024)
- [Transient Fault Tolerant Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2408.16952/) (2024)
- [DeepVigor+: Scalable and Accurate Semi-Analytical Fault Resilience Analysis for Deep Neural Network](https://mlsanity.com/paper/2410.15742/) (2024)
- [FORTALESA: Fault-Tolerant Reconfigurable Systolic Array for DNN Inference](https://mlsanity.com/paper/2503.04426/) (2025)
- [PhD Thesis Summary: Methods for Reliability Assessment and Enhancement of Deep Neural Network Hardware Accelerators](https://mlsanity.com/paper/2603.08724/) (2026)
- [Adaptive Soft Error Protection for Neural Network Processing](https://mlsanity.com/paper/2407.19664/) (2024)
- [EPSILON: Adaptive Fault Mitigation in Approximate Deep Neural Network using Statistical Signatures](https://mlsanity.com/paper/2504.20074/) (2025)
- [SafeCiM: Investigating Resilience of Hybrid Floating-Point Compute-in-Memory Deep Learning Accelerators](https://mlsanity.com/paper/2512.00059/) (2025)
- [RIFT: A Scalable Methodology for LLM Accelerator Fault Assessment using Reinforcement Learning](https://mlsanity.com/paper/2512.09829/) (2025)
- [DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning](https://mlsanity.com/paper/2603.14729/) (2026)
- [CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks](https://mlsanity.com/paper/2607.15753/) (2026)
- [CheckOne: Lightweight Fault Detection and Mitigation for Vision Transformers](https://mlsanity.com/paper/2608.04035/) (2026)
- [TreeFI: Value-Aware Statistical Fault Injection for Deep Neural Networks](https://mlsanity.com/paper/2609.04912/) (2026)
- [Carry-Through Checksum: A Lightweight Fault-Detection for CNN Inference at the Edge](https://mlsanity.com/paper/2609.16742/) (2026)
- [Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent](https://mlsanity.com/paper/2609.35400/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) and, in particular, Machine Learning (ML) have emerged to be utilized in various applications due to their capability to learn how to solve complex problems. Over the last decade, rapid advances in ML have presented Deep Neural Networks (DNNs) consisting of a large number of neurons and layers. DNN Hardware Accelerators (DHAs) are leveraged to deploy DNNs in the target applications. Safety-critical applications, where hardware faults/errors would result in catastrophic consequences, also benefit from DHAs. Therefore, the reliability of DNNs is an essential subject of research. In recent years, several studies have been published accordingly to assess the reliability of DNNs. In this regard, various reliability assessment methods have been proposed on a variety of platforms and applications. Hence, there is a need to summarize the state of the art to identify the gaps in the study of the reliability of DNNs. In this work, we conduct a Systematic Literature Review (SLR) on the reliability assessment methods of DNNs to collect relevant research works as much as possible, present a categorization of them, and address the open challenges. Through this SLR, three kinds of methods for reliability assessment of DNNs are identified including Fault Injection (FI), Analytical, and Hybrid methods. Since the majority of works assess the DNN reliability by FI, we characterize different approaches and platforms of the FI method comprehensively. Moreover, Analytical and Hybrid methods are propounded. Thus, different reliability assessment methods for DNNs have been elaborated on their conducted DNN platforms and reliability evaluation metrics. Finally, we highlight the advantages and disadvantages of the identified methods and address the open challenges in the research area.