An Insight into Fault Propagation in Deep Neural Networks: Work-in-Progress

Reliability is of critical importance for Deep Neural Networks (DNNs) applied in safety-critical applications. Traditional analysis of fault propagation in DNNs is not suitable for such applications. In this paper we approach to give the theory-driven analysis of fault propagation in DNN. Specifically, the perturbation on weights of layers are formulated and the propagation conditions of faults are obtained through theoretical derivation. All the analysis is based on DNNs with 32-bit float numbers. Finally, initial experiments on three typical DNNs are conducted to evaluate our theoretical results.