# Enhancing Fault Resilience of QNNs by Selective Neuron Splitting

arXiv: https://arxiv.org/abs/2306.09973 · HTML: https://mlsanity.com/paper/2306.09973/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review](https://mlsanity.com/paper/1912.11676/) (2019)
- [HarDNN: Feature Map Vulnerability Evaluation in CNNs](https://mlsanity.com/paper/2002.09786/) (2020)
- [FAT: Training Neural Networks for Reliable Inference Under Hardware Faults](https://mlsanity.com/paper/2011.05873/) (2020)
- [DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment](https://mlsanity.com/paper/2303.06931/) (2023)

## Built on by (most cited first, 6 of 6)

- [MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing](https://mlsanity.com/paper/2306.11758/) (2023)
- [Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning](https://mlsanity.com/paper/2405.10658/) (2024)
- [ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs](https://mlsanity.com/paper/2406.06313/) (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)

## Abstract (arXiv)

The superior performance of Deep Neural Networks (DNNs) has led to their application in various aspects of human life. Safety-critical applications are no exception and impose rigorous reliability requirements on DNNs. Quantized Neural Networks (QNNs) have emerged to tackle the complexity of DNN accelerators, however, they are more prone to reliability issues. In this paper, a recent analytical resilience assessment method is adapted for QNNs to identify critical neurons based on a Neuron Vulnerability Factor (NVF). Thereafter, a novel method for splitting the critical neurons is proposed that enables the design of a Lightweight Correction Unit (LCU) in the accelerator without redesigning its computational part. The method is validated by experiments on different QNNs and datasets. The results demonstrate that the proposed method for correcting the faults has a twice smaller overhead than a selective Triple Modular Redundancy (TMR) while achieving a similar level of fault resiliency.