Winograd Convolution: A Perspective from Fault Tolerance
- Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
- University of Chinese Academy of Sciences（中国科学院大学）
- Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）
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英文摘要
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
