Few-shot graph learning with robust and energy-efficient memory-augmented graph neural network (MAGNN) based on homogeneous computing-in-memory
Woyu Zhang(Chinese Academy of Sciences), Ming Liu(China Academy of Space Technology), Nanjia Jiang(Chinese Academy of Sciences), Danian Dong(Chinese Academy of Sciences), Xiaoxin Xu(Shanghai University), Shaocong Wang(World Wide Web Consortium), Chunmeng Dou(Chinese Academy of Sciences), Kai Ni(Rochester Institute of Technology), Zhongrui Wang(Huazhong Agricultural University), Yi Li(Huazhong Agricultural University), Fei Wang(Chinese Academy of Sciences), Dashan Shang(University of Hong Kong), Zeyu Guo(Jinzhou Medical University), Renrui Fang(Chinese Academy of Sciences)
2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)
June 12, 2022
Cited by 11
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