# FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation

arXiv: https://arxiv.org/abs/1912.00941 · HTML: https://mlsanity.com/paper/1912.00941/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 30 of 31)

- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework](https://mlsanity.com/paper/2109.09829/) (2021)
- [FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions](https://mlsanity.com/paper/2112.13544/) (2021)
- [Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators](https://mlsanity.com/paper/2401.09509/) (2024)
- [Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective](https://mlsanity.com/paper/2412.03630/) (2024)
- [Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision](https://mlsanity.com/paper/2108.07019/) (2021)
- [DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment](https://mlsanity.com/paper/2303.06931/) (2023)
- [Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators](https://mlsanity.com/paper/2104.08323/) (2021)
- [R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors](https://mlsanity.com/paper/2107.03096/) (2021)
- [Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems](https://mlsanity.com/paper/2204.09514/) (2022)
- [A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks](https://mlsanity.com/paper/2310.20349/) (2023)
- [Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks](https://mlsanity.com/paper/2105.03251/) (2021)
- [enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks](https://mlsanity.com/paper/2208.00328/) (2022)
- [Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques](https://mlsanity.com/paper/2309.16733/) (2023)
- [Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead](https://mlsanity.com/paper/2205.14420/) (2022)
- [Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning](https://mlsanity.com/paper/2405.10658/) (2024)
- [LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Models](https://mlsanity.com/paper/2511.02866/) (2025)
- [PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors](https://mlsanity.com/paper/2212.03475/) (2022)
- [DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation](https://mlsanity.com/paper/2504.05122/) (2025)
- [Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors](https://mlsanity.com/paper/2307.10244/) (2023)
- [Global Clipper: Enhancing Safety and Reliability of Transformer-based Object Detection Models](https://mlsanity.com/paper/2406.03229/) (2024)
- [ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs](https://mlsanity.com/paper/2406.06313/) (2024)
- [ZOBNN: Zero-Overhead Dependable Design of Binary Neural Networks with Deliberately Quantized Parameters](https://mlsanity.com/paper/2407.04964/) (2024)
- [Transient Fault Tolerant Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2408.16952/) (2024)
- [FORTALESA: Fault-Tolerant Reconfigurable Systolic Array for DNN Inference](https://mlsanity.com/paper/2503.04426/) (2025)
- [FLARE: One-Shot PE-Level Fault Localization in Systolic Arrays via Algebraic Test Vectors](https://mlsanity.com/paper/2605.08594/) (2026)
- [Cross-Layer Optimization for Fault-Tolerant Deep Learning](https://mlsanity.com/paper/2312.13754/) (2023)
- [Dependability Evaluation of Stable Diffusion with Soft Errors on the Model Parameters](https://mlsanity.com/paper/2404.00352/) (2024)
- [Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embedded Machine Learning Accelerator](https://mlsanity.com/paper/2404.09317/) (2024)

## Abstract (arXiv)

Deep Neural Networks (DNNs) are widely being adopted for safety-critical applications, e.g., healthcare and autonomous driving. Inherently, they are considered to be highly error-tolerant. However, recent studies have shown that hardware faults that impact the parameters of a DNN (e.g., weights) can have drastic impacts on its classification accuracy. In this paper, we perform a comprehensive error resilience analysis of DNNs subjected to hardware faults (e.g., permanent faults) in the weight memory. The outcome of this analysis is leveraged to propose a novel error mitigation technique which squashes the high-intensity faulty activation values to alleviate their impact. We achieve this by replacing the unbounded activation functions with their clipped versions. We also present a method to systematically define the clipping values of the activation functions that result in increased resilience of the networks against faults. We evaluate our technique on the AlexNet and the VGG-16 DNNs trained for the CIFAR-10 dataset. The experimental results show that our mitigation technique significantly improves the resilience of the DNNs to faults. For example, the proposed technique offers on average 68.92% improvement in the classification accuracy of resilience-optimized VGG-16 model at 1e-5 fault rate, when compared to the base network without any fault mitigation.