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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…
MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing
Haitong Huang, Cheng Liu, Bo Liu +3
To ensure resilient neural network processing on even unreliable hardware, comprehensive reliability analysis against various hardware faults is generally required before the deep…
Statistical Modeling of Soft Error Influence on Neural Networks
Haitong Huang, Xinghua Xue, Cheng Liu +5
Soft errors in large VLSI circuits pose dramatic influence on computing- and memory-intensive neural network (NN) processing. Understanding the influence of soft errors on NNs is c…
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…