# Winograd Convolution: A Perspective from Fault Tolerance

arXiv: https://arxiv.org/abs/2202.08675 · HTML: https://mlsanity.com/paper/2202.08675/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Fast Algorithms for Convolutional Neural Networks](https://mlsanity.com/paper/1509.09308/) (2015)
- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications](https://mlsanity.com/paper/2004.01743/) (2020)
- [Efficient Sparse-Winograd Convolutional Neural Networks](https://mlsanity.com/paper/1802.06367/) (2018)
- [ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators](https://mlsanity.com/paper/1802.03806/) (2018)
- [Searching for Winograd-aware Quantized Networks](https://mlsanity.com/paper/2002.10711/) (2020)
- [Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs](https://mlsanity.com/paper/2104.09455/) (2021)
- [AxTrain: Hardware-Oriented Neural Network Training for Approximate Inference](https://mlsanity.com/paper/1805.08309/) (2018)
- [FTT-NAS: Discovering Fault-Tolerant Convolutional Neural Architecture](https://mlsanity.com/paper/2003.10375/) (2020)
- [Training Modern Deep Neural Networks for Memory-Fault Robustness](https://mlsanity.com/paper/1911.10287/) (2019)
- [R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors](https://mlsanity.com/paper/2107.03096/) (2021)
- [DWM: A Decomposable Winograd Method for Convolution Acceleration](https://mlsanity.com/paper/2002.00552/) (2020)
- [CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors](https://mlsanity.com/paper/1903.01042/) (2019)
- [LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units](https://mlsanity.com/paper/2003.08646/) (2020)

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

- [Fault-Tolerant Deep Learning: A Hierarchical Perspective](https://mlsanity.com/paper/2204.01942/) (2022)
- [MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing](https://mlsanity.com/paper/2306.11758/) (2023)
- [Statistical Modeling of Soft Error Influence on Neural Networks](https://mlsanity.com/paper/2210.05876/) (2022)
- [Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance](https://mlsanity.com/paper/2308.08230/) (2023)
- [Cross-Layer Optimization for Fault-Tolerant Deep Learning](https://mlsanity.com/paper/2312.13754/) (2023)
- [Adaptive Soft Error Protection for Neural Network Processing](https://mlsanity.com/paper/2407.19664/) (2024)

## Abstract (arXiv)

Winograd convolution is originally proposed to reduce the computing overhead by converting multiplication in neural network (NN) with addition via linear transformation. Other than the computing efficiency, we observe its great potential in improving NN fault tolerance and evaluate its fault tolerance comprehensively for the first time. Then, we explore the use of fault tolerance of winograd convolution for either fault-tolerant or energy-efficient NN processing. According to our experiments, winograd convolution can be utilized to reduce fault-tolerant design overhead by 27.49% or energy consumption by 7.19% without any accuracy loss compared to that without being aware of the fault tolerance