Graph Neural Networks for EDA
Generic graph neural network forms are often inadequate unless they are adapted to circuit structure. Public industrial evidence for GNN-specific EDA methods remains narrow. Graph neural network performance in EDA is strongest when model c…
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Generic graph neural network forms are often inadequate unless they are adapted to circuit structure. Public industrial evidence for GNN-specific EDA methods remains narrow. Graph neural network performance in EDA is strongest when model computation aligns with the algebra and constraints of the target task. Circuit graphs should shape model architecture because they are directed, heterogeneous, multi-scale, physically embedded, and stage-dependent.