GNN-Based Hierarchical Annotation for Analog Circuits

Kunal, Kishor; Dhar, Tonmoy; Madhusudan, Meghna; Poojary, Jitesh; Sharma, Arvind K.; Xu, Wenbin; Burns, Steven; Hu, Jiang; Harjani, Ramesh; Sapatnekar, Sachin S.

doi:10.1109/tcad.2023.3236269

Cited by 6 publications

(2 citation statements)

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“…The social demand for automated circuit design has increased and GNNs have emerged as a promising solution for semiconductor applications [11]- [20]. The utilization of GNNs is driven by the increasing difficulty in adhering to stringent design rules, coupled with an increase in design cost owing to miniaturization and adoption of low-voltage transistors [11] [21].…”

Section: A Previous Workmentioning

confidence: 99%

“…Circuits, however, lack spatial coordinates, such as images and natural language; therefore, they should be treated as graph networks that do not rely on a coordinate system [10] [11] [12]. Unlike continuous values in images and natural language, graph networks have discrete values that allow for a degree of freedom in the graph transformation method.…”

Section: Introductionmentioning

confidence: 99%

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Circuit2Graph: Circuits With Graph Neural Networks

Yamakaji,

Shouno,

Fukushima

2024

IEEE Access

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Circuit design requires trial and error in both prototyping and simulation owing to the high degrees of freedom and mutual interference between the components. In this study, we propose a novel approach to address this challenge by introducing a transformation method that converts circuits into graph networks. This transformation is achieved with no loss of information, and we evaluate its accuracy through the application of graph classification by utilizing graph neural networks. We assume that the information degradation can result from self-loops, multi-edges, and heterogeneous graphs. To mitigate these issues, we propose a method that effectively reduces their impact. The results of this study demonstrate the effectiveness of our proposed method, as it achieves an accuracy of 97.89%. This represents a significant improvement of 5.2% when compared with the conventional method. Notably, our proposed method is applicable to generalpurpose circuits. This makes it a valuable addition to the existing repertoire of circuit solution methods, alongside analytical and simulation approaches.

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Section: A Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%