An ST-GCN predicts multi-cycle fault impact probabilities in sequential circuits from graph structure and temporal features, cutting analysis time versus full fault simulation, though the key validation is partly circular.
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A Spatio-Temporal Graph Neural Networks Approach for Predicting Silent Data Corruption inducing Circuit-Level Faults
An ST-GCN predicts multi-cycle fault impact probabilities in sequential circuits from graph structure and temporal features, cutting analysis time versus full fault simulation, though the key validation is partly circular.