most citedApproxABFT: Approximate Algorithm-Based Fault Tolerance for Neural Network Processing

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cs.LG2023

Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance

Xinghua Xue, Cheng Liu, Bo Liu +6

Winograd is generally utilized to optimize convolution performance and computational efficiency because of the reduced multiplication operations, but the reliability issues brought…

cs.LG2022

Winograd Convolution: A Perspective from Fault Tolerance

Xinghua Xue, Haitong Huang, Cheng Liu +3

Winograd convolution is originally proposed to reduce the computing overhead by converting multiplication in neural network (NN) with addition via linear transformation. Other than…