论文标题
Winograd卷积：容忍度的视角
Winograd Convolution: A Perspective from Fault Tolerance
论文作者
论文摘要
Winograd卷积最初是为了通过线性转换加入神经网络（NN）中的繁殖来减少计算开销。除了计算效率之外，我们还观察到它在提高NN容错性和首次全面评估其容错性方面的巨大潜力。然后，我们探索Winograd卷积的可容忍度用于容忍或节能的NN处理。根据我们的实验，可以将Winograd卷积用于将容忍度的设计开销降低27.49 \％或能耗降低7.19 \％，而没有任何准确的损失，而没有任何准确的损失
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
