A Fault Injection Method for Evaluating Single Event Soft Errors in Deep Neural Networks

Single Event Effects (SEEs) pose significant reliability challenges to deep neural networks (DNNs), particularly in aerospace environments characterized by radiation exposure. This paper introduces a comprehensive fault injection (FI) framework for systematic vulnerability assessment and mitigation of SEEs in DNN architectures. We propose a Progressive Bit Search (PBS) technique to efficiently identify sensitive layers within DNNs, combined with Triple Modular Redundancy (TMR) for hardening critical layers. Experimental results on YOLOv5 demonstrate that our approach improves fault injection evaluation efficiency by over tenfold while enhancing SEE hardening capability by more than 24 times, with a resource cost increase of less than 10%. This work addresses the critical gap between software-based FI simulations and hardware-accurate reliability assessment, providing a practical solution for radiation-hardened DNN design in aerospace applications.