# Fast and Accurate Error Simulation for CNNs against Soft Errors

arXiv: https://arxiv.org/abs/2206.02051 · HTML: https://mlsanity.com/paper/2206.02051/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 50 references that are in the map.
- First posted on arXiv 4 Jun 2022.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications](https://mlsanity.com/paper/2004.01743/) (2020)
- [Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networks](https://mlsanity.com/paper/1904.04375/) (2019)

## Built on by (most cited first, 7 of 7)

- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques](https://mlsanity.com/paper/2309.16733/) (2023)
- [ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs](https://mlsanity.com/paper/2406.06313/) (2024)
- [FORTALESA: Fault-Tolerant Reconfigurable Systolic Array for DNN Inference](https://mlsanity.com/paper/2503.04426/) (2025)
- [Concurrent Linguistic Error Detection (CLED): a New Methodology for Error Detection in Large Language Models](https://mlsanity.com/paper/2403.16393/) (2024)
- [CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks](https://mlsanity.com/paper/2607.15753/) (2026)
- [TreeFI: Value-Aware Statistical Fault Injection for Deep Neural Networks](https://mlsanity.com/paper/2609.04912/) (2026)

## Abstract (arXiv)

The great quest for adopting AI-based computation for safety-/mission-critical applications motivates the interest towards methods for assessing the robustness of the application w.r.t. not only its training/tuning but also errors due to faults, in particular soft errors, affecting the underlying hardware. Two strategies exist: architecture-level fault injection and application-level functional error simulation. We present a framework for the reliability analysis of Convolutional Neural Networks (CNNs) via an error simulation engine that exploits a set of validated error models extracted from a detailed fault injection campaign. These error models are defined based on the corruption patterns of the output of the CNN operators induced by faults and bridge the gap between fault injection and error simulation, exploiting the advantages of both approaches. We compared our methodology against SASSIFI for the accuracy of functional error simulation w.r.t. fault injection, and against TensorFI in terms of speedup for the error simulation strategy. Experimental results show that our methodology achieves about 99% accuracy of the fault effects w.r.t. SASSIFI, and a speedup ranging from 44x up to 63x w.r.t. TensorFI, that only implements a limited set of error models.