ReGNN: a ReRAM-based heterogeneous architecture for general graph neural networks
Published in DAC, 2022
Recommended citation: Cong Liu, Haikun Liu, Hai Jin, Xiaofei Liao, Yu Huang, Zhuohui Duan, Jiahong Xu, and Huize Li. (2022). "ReGNN: a ReRAM-based heterogeneous architecture for general graph neural networks." In Proceedings of the 59th ACM/IEEE Design Automation Conference (DAC). pp. 469-474.
Download Paper