Winograd is generally utilized to optimize convolution performance and computational efficiency because of the reduced multiplication operations, but the reliability issues brought by winograd are usually overlooked. In this work, we observe the great potential of winograd convolution in improving neural network (NN) fault tolerance. Based on the observation, we evaluate winograd convolution fault tolerance comprehensively from different granularities ranging from models, layers, and operation types for the first time. Then, we explore the use of inherent fault tolerance of winograd convolution for cost-effective NN protection against soft errors. Specifically, we mainly investigate how winograd convolution can be effectively incorporated with classical fault-tolerant design approaches including triple modular redundancy (TMR), fault-aware retraining, and constrained activation functions. According to our experiments, winograd convolution can reduce the fault-tolerant design overhead by 55.77\% on average without any accuracy loss compared to standard convolution, and further reduce the computing overhead by 17.24\% when the inherent fault tolerance of winograd convolution is considered. When it is applied on fault-tolerant neural networks enhanced with fault-aware retraining and constrained activation functions, the resulting model accuracy generally shows significant improvement in presence of various faults.

翻译：Winograd通常因减少乘法运算而被用于优化卷积性能与计算效率，但其带来的可靠性问题常被忽视。在本工作中，我们观察到Winograd卷积在提升神经网络容错性方面具有巨大潜力。基于这一发现，我们首次从模型、层和操作类型等不同粒度全面评估了Winograd卷积的容错能力。随后，我们探索利用Winograd卷积的固有容错特性，实现针对软错误的成本效益型神经网络保护。具体而言，我们主要研究如何将Winograd卷积有效融入经典容错设计方法，包括三模冗余（TMR）、容错感知重训练和约束激活函数。实验表明，相比标准卷积，Winograd卷积可在无精度损失的情况下平均降低55.77%的容错设计开销；若考虑其固有容错特性，可进一步降低17.24%的计算开销。当将其应用于经容错感知重训练和约束激活函数增强的容错神经网络时，所得模型精度在各种故障场景下均呈现显著提升。