著者
Motoki Amagasaki Hiroki Oyama Yuichiro Fujishiro Masahiro Iida Hiroaki Yasuda Hiroto Ito
出版者
Information Processing Society of Japan
雑誌
IPSJ Transactions on System LSI Design Methodology (ISSN:18826687)
巻号頁・発行日
vol.13, pp.69-71, 2020 (Released:2020-08-13)
参考文献数
7

Graph neural networks are a type of deep-learning model for classification of graph domains. To infer arithmetic functions in a netlist, we applied relational graph convolutional networks (R-GCN), which can directly treat relations between nodes and edges. However, because original R-GCN supports only for node level labeling, it cannot be directly used to infer set of functions in a netlist. In this paper, by considering the distribution of labels for each node, we show a R-GCN based function inference method and data augmentation technique for netlist having multiple functions. According to our result, 91.4% accuracy is obtained from 1, 000 training data, thus demonstrating that R-GCN-based methods can be effective for graphs with multiple functions.

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R-GCN Based Function Inference for Gate-level Netlist https://t.co/JsSkNufKlu #maskotlib

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