  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
