no code implementations • 16 Aug 2023 • Xinghua Xue, Cheng Liu, Bo Liu, Haitong Huang, Ying Wang, Tao Luo, Lei Zhang, Huawei Li, Xiaowei Li
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.
1 code implementation • 20 Jun 2023 • Haitong Huang, Cheng Liu, Bo Liu, Xinghua Xue, Huawei Li, Xiaowei Li
It enables users to modify an independent fault configuration file rather than neural network models for the fault injection and vulnerability analysis.
no code implementations • 12 Oct 2022 • Haitong Huang, Xinghua Xue, Cheng Liu, Ying Wang, Tao Luo, Long Cheng, Huawei Li, Xiaowei Li
Prior work mainly rely on fault simulation to analyze the influence of soft errors on NN processing.
no code implementations • 17 Feb 2022 • Xinghua Xue, Haitong Huang, Cheng Liu, Ying Wang, Tao Luo, Lei Zhang
Winograd convolution is originally proposed to reduce the computing overhead by converting multiplication in neural network (NN) with addition via linear transformation.