# EPSILON: Adaptive Fault Mitigation in Approximate Deep Neural Network using Statistical Signatures

arXiv: https://arxiv.org/abs/2504.20074 · HTML: https://mlsanity.com/paper/2504.20074/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 42 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [Depth-Adaptive Transformer](https://mlsanity.com/paper/1910.10073/) (2019)
- [EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism](https://mlsanity.com/paper/2312.04916/) (2023)
- [ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining](https://mlsanity.com/paper/1907.07229/) (2019)
- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI](https://mlsanity.com/paper/2106.09166/) (2021)
- [Adaptive Deep Neural Network Inference Optimization with EENet](https://mlsanity.com/paper/2301.07099/) (2023)
- [Exploring Fault-Energy Trade-offs in Approximate DNN Hardware Accelerators](https://mlsanity.com/paper/2101.02860/) (2021)
- [Hierarchical Training of Deep Neural Networks Using Early Exiting](https://mlsanity.com/paper/2303.02384/) (2023)
- [Exposing Reliability Degradation and Mitigation in Approximate DNNs under Permanent Faults](https://mlsanity.com/paper/2301.07484/) (2023)
- [DE3-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks](https://mlsanity.com/paper/2402.05948/) (2024)
- [eFAT: Improving the Effectiveness of Fault-Aware Training for Mitigating Permanent Faults in DNN Hardware Accelerators](https://mlsanity.com/paper/2304.12949/) (2023)

## Abstract (arXiv)

The increasing adoption of approximate computing in deep neural network accelerators (AxDNNs) promises significant energy efficiency gains. However, permanent faults in AxDNNs can severely degrade their performance compared to their accurate counterparts (AccDNNs). Traditional fault detection and mitigation approaches, while effective for AccDNNs, introduce substantial overhead and latency, making them impractical for energy-constrained real-time deployment. To address this, we introduce EPSILON, a lightweight framework that leverages pre-computed statistical signatures and layer-wise importance metrics for efficient fault detection and mitigation in AxDNNs. Our framework introduces a novel non-parametric pattern-matching algorithm that enables constant-time fault detection without interrupting normal execution while dynamically adapting to different network architectures and fault patterns. EPSILON maintains model accuracy by intelligently adjusting mitigation strategies based on a statistical analysis of weight distribution and layer criticality while preserving the energy benefits of approximate computing. Extensive evaluations across various approximate multipliers, AxDNN architectures, popular datasets (MNIST, CIFAR-10, CIFAR-100, ImageNet-1k), and fault scenarios demonstrate that EPSILON maintains 80.05% accuracy while offering 22% improvement in inference time and 28% improvement in energy efficiency, establishing EPSILON as a practical solution for deploying reliable AxDNNs in safety-critical edge applications.