Towards SEU Fault Propagation Prediction with Spatio-Temporal Graph Convolutional Networks
Li Lu, Junchao Chen, Markus Ulbricht, Milos D. Krstic · 2024
Assessing Single Event Upset (SEU) sensitivity in complex circuits is increasingly important but challenging. This paper proposes an efficient approach using Spatio-temporal Graph Convolutional Networks (STGCN) to predict the results of SEU simulation-based fault injection. Representing circuit structures as graphs and integrating temporal data from the workload's waveform into these graphs, STGCN achieves a 94-96% prediction accuracy on four test circuits.