# Making Convolutions Resilient via Algorithm-Based Error Detection Techniques

arXiv: https://arxiv.org/abs/2006.04984 · HTML: https://mlsanity.com/paper/2006.04984/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions](https://mlsanity.com/paper/2112.13544/) (2021)
- [Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs](https://mlsanity.com/paper/2104.09455/) (2021)
- [R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors](https://mlsanity.com/paper/2107.03096/) (2021)
- [Efficient Soft-Error Detection for Low-precision Deep Learning Recommendation Models](https://mlsanity.com/paper/2103.00130/) (2021)
- [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)
- [MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing](https://mlsanity.com/paper/2306.11758/) (2023)
- [Custom Algorithm-based Fault Tolerance for Attention Layers in Transformers](https://mlsanity.com/paper/2507.16676/) (2025)
- [Hardware Resilience Properties of Text-Guided Image Classifiers](https://mlsanity.com/paper/2311.14062/) (2023)
- [Transient Fault Tolerant Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2408.16952/) (2024)
- [Syndrome Decoding for Silent Data Corruption in Quantized Integer GPU Arithmetic](https://mlsanity.com/paper/2609.19743/) (2026)

## Abstract (arXiv)

The ability of Convolutional Neural Networks (CNNs) to accurately process real-time telemetry has boosted their use in safety-critical and high-performance computing systems. As such systems require high levels of resilience to errors, CNNs must execute correctly in the presence of hardware faults. Full duplication provides the needed assurance but incurs a prohibitive 100% overhead. Algorithmic techniques are known to offer low-cost solutions, but the practical feasibility and performance of such techniques have never been studied for CNN deployment platforms (e.g., TensorFlow or TensorRT on GPUs). In this paper, we focus on algorithmically verifying Convolutions, which are the most resource-demanding operations in CNNs. We use checksums to verify convolutions, adding a small amount of redundancy, far less than full-duplication. We first identify the challenges that arise in employing Algorithm-Based Error Detection (ABED) for Convolutions in optimized inference platforms that fuse multiple network layers and use reduced-precision operations, and demonstrate how to overcome them. We propose and evaluate variations of ABED techniques that offer implementation complexity, runtime overhead, and coverage trade-offs. Results show that ABED can detect all transient hardware errors that might otherwise corrupt output and does so while incurring low runtime overheads (6-23%), offering at least 1.6X throughput to workloads compared to full duplication.