# DeepVigor+: Scalable and Accurate Semi-Analytical Fault Resilience Analysis for Deep Neural Network

arXiv: https://arxiv.org/abs/2410.15742 · HTML: https://mlsanity.com/paper/2410.15742/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 55 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016)
- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [Convolution-Weight-Distribution Assumption: Rethinking the Criteria of Channel Pruning](https://mlsanity.com/paper/2004.11627/) (2020)
- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [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)
- [DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment](https://mlsanity.com/paper/2303.06931/) (2023)
- [Enhancing Fault Resilience of QNNs by Selective Neuron Splitting](https://mlsanity.com/paper/2306.09973/) (2023)
- [APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors](https://mlsanity.com/paper/2305.19733/) (2023)
- [SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators](https://mlsanity.com/paper/2403.02946/) (2024)
- [Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques](https://mlsanity.com/paper/2309.16733/) (2023)

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

- [TreeFI: Value-Aware Statistical Fault Injection for Deep Neural Networks](https://mlsanity.com/paper/2609.04912/) (2026)

## Abstract (arXiv)

The growing exploitation of Machine Learning (ML) in safety-critical applications necessitates rigorous safety analysis. Hardware reliability assessment is a major concern with respect to measuring the level of safety in ML-based systems. Quantifying the reliability of emerging ML models, including Convolutional Neural Networks (CNNs), is highly complex due to their enormous size in terms of the number of parameters and computations. Conventionally, Fault Injection (FI) is applied to perform a reliability measurement. However, performing FI on modern-day CNNs is prohibitively time-consuming if an acceptable confidence level is to be achieved. To speed up FI for large CNNs, statistical FI (SFI) has been proposed, but its runtimes are still considerably long. In this work, we introduce DeepVigor+, a scalable, fast, and accurate semi-analytical method as an efficient alternative for reliability measurement in CNNs. DeepVigor+ implements a fault propagation analysis model and attempts to acquire Vulnerability Factors (VFs) as reliability metrics in an optimal way. The results indicate that DeepVigor+ obtains VFs for CNN models with an error less than 1%, i.e., the objective in SFI, but with 14.9 up to 26.9 times fewer simulations than the best-known state-of-the-art SFI. DeepVigor+ enables an accurate reliability analysis for large and deep CNNs within a few minutes, rather than achieving the same results in days or weeks.