A Hierarchical Assessment Strategy on Soft Error Propagation in Deep Learning Controller

Abstract

Deep learning techniques have been introduced into the field of intelligent controller design in recent years and become an effective alternative in complex control scenarios. In addition to improve control robustness, deep learning controllers (DLCs) also provide a potential fault tolerance to internal disturbances (such as soft errors) due to the inherent redundant structure of deep neural networks (DNNs). In this paper, we propose a hierarchical assessment to characterize the impact of soft errors on the dependability of a PID controller and its DLC alternative. Single-bit-flip injections in underlying hardware and time series data collection from multiple abstraction layers (ALs) are performed on a virtual prototype system based on an ARM Cortex-A9 CPU, with a PID controller and corresponding recurrent neural network (RNN) implemented DLC deployed on it. We employ generative adversarial networks and Bayesian networks to characterize the local and global dependencies caused by soft errors across the system. By analyzing cross-AL fault propagation paths and component sensitivities, we discover that the parallel data processing pipelines and regular feature size scaling mechanism in DLC can effectively prevent critical failure causing faults from propagating to the control output.