To evaluate the impact of soft errors on convolutional neural networks (CNNs) deployed in edged computation systems, we propose a data-driven assessment strategy to characterize the propagation flow across hardware and software abstraction layers of the system in an interpretable way. Single-bit-flip injections in underlying hardware architecture are performed on virtual embedded system with a CNN-based image classifier deployed on it. We depict the local activation and global dependencies caused by soft errors across the system in form of a directed acyclic graph by using generative adversarial networks and Bayesian networks as data modeling methods. The cross-layer fault propagation paths and component sensitivities show that the deep neural networks like CNNs can effectively prevent the faults that may cause critical failures from propagating to the system output via the channel sparsity and regular pooling mechanism in the network pipelines.

为了评估软错误对部署在边缘计算系统中的卷积神经网络（CNN）的影响，我们提出了一种数据驱动的评估策略，以一种可解释的方式表征了跨系统软硬件抽象层的传播流。基础硬件体系结构中的单比特翻转注入是在虚拟嵌入式系统上执行的，该虚拟嵌入式系统上部署了基于CNN的图像分类器。我们通过使用生成对抗网络和贝叶斯网络作为数据建模方法，以有向无环图的形式描述了整个系统中由软错误引起的局部激活和全局依赖性。