ReGNN: a ReRAM-based heterogeneous architecture for general graph neural networks - researchr publication authors

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Cong Liu, Haikun Liu, Hai Jin 0001, Xiaofei Liao, Yu Zhang, Zhuohui Duan, Jiahong Xu, Huize Li. ReGNN: a ReRAM-based heterogeneous architecture for general graph neural networks. In Rob Oshana, editor, DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10 - 14, 2022. pages 469-474, ACM, 2022. [doi]

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