Winograd Convolution: A Perspective from Fault Tolerance
TimeWednesday, July 13th4:18pm - 4:42pm PDT
Location3007, Level 3
Event Type
Research Manuscript
Manufacturing Test and Reliability
DescriptionWinograd 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 56.8% or energy consumption by 8.6% without any accuracy loss compared to that without being aware of the fault tolerance.