2 citations · 4 across the 7 of their papers we have counts for
7 papers
Adaptive Soft Error Protection for Neural Network Processing
Xinghua Xue, Cheng Liu, Feng Min +1
Previous research on selective protection for neural network components typically exploits only static vulnerability differences. Although these methods improve upon classical modu…
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…
ApproxABFT: Approximate Algorithm-Based Fault Tolerance for Neural Network Processing
Xinghua Xue, Cheng Liu, Feng Min +2
With the increasing deployment of deep neural networks (DNNs) in terrestrial and aerospace safety-critical applications, system reliability has emerged as a co-equal design metric…
Soft Error Reliability Analysis of Vision Transformers
Xinghua Xue, Cheng Liu, Ying Wang +5
Vision Transformers (ViTs) that leverage self-attention mechanism have shown superior performance on many classical vision tasks compared to convolutional neural networks (CNNs) an…
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…