Few-shot Graph Learning with Robust and Energy-Efficient Memory-Augmented Graph Neural Network (MAGNN) Based on Homogeneous Computing-in-memory.

Woyu Zhang,Shaocong Wang,Yi Li,Xiaoxin Xu,Danian Dong,Nanjia Jiang,Fei Wang,Zeyu Guo,Renrui Fang,Chunmeng Dou,Kai Ni,Zhongrui Wang,Dashan Shang,Ming Liu

2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)（2022）

引用 8|浏览32

关键词

1T1R resistive random-access memory,in-memory computing paradigm,high end-to-end accuracy,few-shot graph learning,robust energy-efficient memory,smart edge devices,chip-level demonstration,controller,associative memory,energy-efficient memory-augmented graph neural network,homogeneous computing-in-memory,graph structured data learning,RRAM,node classification,CORA dataset,energy consumption,conventional digital systems,MAGNN

AI 理解论文

溯源树

样例

生成溯源树，研究论文发展脉络

Chat Paper

正在生成论文摘要