A robust automated analog circuits classification involving a graph neural network and a novel data augmentation strategy

HIGHLIGHTS

SUMMARY

A particular example of recent and powerful ML models is the so-called Graph Neural_Networks GCNs. The comprehensive modeling of an analog circuit unto a graph model, after an appropriate related ontology formulation, and design, enables the involvement and tuning of an appropriate graph neural_network architecture for ensuring a robust automated classification of analog circuits. Besides a comprehensive ontology for mapping an analog circuit into a graph, a general methodology for building a graph classification pipeline for a limited-size dataset is introduced in Section 4. For the purpose of limiting the user inputs . . .

 



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