Search Results for author:
Found 4 papers, 1 papers with code
Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance
When it is applied on fault-tolerant neural networks enhanced with fault-aware retraining and constrained activation functions, the resulting model accuracy generally shows significant improvement in presence of various faults.
MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing
It enables users to modify an independent fault configuration file rather than neural network models for the fault injection and vulnerability analysis.
Statistical Modeling of Soft Error Influence on Neural Networks
Prior work mainly rely on fault simulation to analyze the influence of soft errors on NN processing.
Winograd Convolution: A Perspective from Fault Tolerance
Winograd convolution is originally proposed to reduce the computing overhead by converting multiplication in neural network (NN) with addition via linear transformation.
