Quantifying the Impact of Single-Event Upsets on Neural Networks with Uncertainty Analysis

Abstract The Convolutional neural networks have gradually penetrated into various fields of social life, in addition to aerospace devices, neural network chips are also indispensable for target recognition and detection, etc. Due to the complex environment in space, neural network chips are extremely easy to be damaged by cosmic rays, so it is urgent to evaluate and enhance the robustness of neural network chips. However, most of the existing researches are monolithic error injection of neural networks and do not build relevant models for quantitative analysis. The goal of this paper is to analyse the effects of single-event upsets (SEUs) induced by the penetration of SRAM storage areas in a quantized neural network chip by energetic particles from space. Firstly, a SEU probability model as well as an uncertainty assessment model are established, and the effect of SEU on the reliability of CNNs is evaluated by fault injection with the upset rate of four chip types. The results of the error injection experiments show that the Virtex-5 type of rollover rate decreases the average accuracy of VGG-16 by 20%, while the other three types of error injection have an impact of about 11% on the accuracy of the neural network. Quantitative experimental results confirm the hypothesis that SEU increases the uncertainty of neural networks. The quantification of these effects contributes to the study of reinforcement of neural network chips.