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

20222022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)11 citationsDOI

Abstract

Learning graph structured data from limited examples on-the-fly is a key challenge to smart edge devices. Here, we present the first chip-level demonstration of few-shot graph learning which homogeneously implements both the controller and associative memory of a memory-augmented graph neural network using a 1T1R resistive random-access memory (RRAM). Leveraging the in-memory computing paradigm, we validated the high end-to-end accuracy of 78% (GPU baseline 80%) and robustness on node classification of CORA dataset, while achieved 70-fold reduction in latency and 60-fold reduction in energy consumption compared with conventional digital systems.

Topics & Concepts

Computer scienceGraphRobustness (evolution)On the flyResistive random-access memoryTheoretical computer scienceGeneElectrodePhysical chemistryChemistryBiochemistryOperating systemFerroelectric and Negative Capacitance DevicesAdvanced Memory and Neural ComputingMachine Learning and ELM