# NAPER: Fault Protection for Real-Time Resource-Constrained Deep Neural Networks

arXiv: https://arxiv.org/abs/2504.06591 · HTML: https://mlsanity.com/paper/2504.06591/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Snapshot Ensembles: Train 1, get M for free](https://mlsanity.com/paper/1704.00109/) (2017)
- [Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles](https://mlsanity.com/paper/1606.07839/) (2016)
- [Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?](https://mlsanity.com/paper/1806.07550/) (2018)
- [Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness](https://mlsanity.com/paper/1908.11091/) (2019)
- [Aegis: Mitigating Targeted Bit-flip Attacks against Deep Neural Networks](https://mlsanity.com/paper/2302.13520/) (2023)
- [Resilient Binary Neural Network](https://mlsanity.com/paper/2302.00956/) (2023)
- [Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation](https://mlsanity.com/paper/1908.10017/) (2019)
- [In-Place Zero-Space Memory Protection for CNN](https://mlsanity.com/paper/1910.14479/) (2019)
- [Self-Healing Robust Neural Networks via Closed-Loop Control](https://mlsanity.com/paper/2206.12963/) (2022)
- [MILR: Mathematically Induced Layer Recovery for Plaintext Space Error Correction of CNNs](https://mlsanity.com/paper/2010.14687/) (2020)
- [Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning](https://mlsanity.com/paper/2405.10658/) (2024)

## Built on by (most cited first, 1 of 1)

- [BitFlipScope: Scalable Fault Localization and Recovery for Bit-Flip Corruptions in LLMs](https://mlsanity.com/paper/2512.22174/) (2025)

## Abstract (arXiv)

Fault tolerance in Deep Neural Networks (DNNs) deployed on resource-constrained systems presents unique challenges for high-accuracy applications with strict timing requirements. Memory bit-flips can severely degrade DNN accuracy, while traditional protection approaches like Triple Modular Redundancy (TMR) often sacrifice accuracy to maintain reliability, creating a three-way dilemma between reliability, accuracy, and timeliness. We introduce NAPER, a novel protection approach that addresses this challenge through ensemble learning. Unlike conventional redundancy methods, NAPER employs heterogeneous model redundancy, where diverse models collectively achieve higher accuracy than any individual model. This is complemented by an efficient fault detection mechanism and a real-time scheduler that prioritizes meeting deadlines by intelligently scheduling recovery operations without interrupting inference. Our evaluations demonstrate NAPER's superiority: 40% faster inference in both normal and fault conditions, maintained accuracy 4.2% higher than TMR-based strategies, and guaranteed uninterrupted operation even during fault recovery. NAPER effectively balances the competing demands of accuracy, reliability, and timeliness in real-time DNN applications