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.
Le, James Laudon, Richard Ho, R oger Carpenter, and Jeff Dean
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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.