Few-shot graph learning with robust and energy-efficient memory-augmented graph neural network (MAGNN) based on homogeneous computing-in-memory

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 networ...

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Bibliographic Details
Main Authors: Zhang, Woyu, Wang, Shaocong, Li, Yi, Xu, Xiaoxin, Dong, Danian, Jiang, Nanjia, Wang, Fei, Guo, Zeyu, Fang, Renrui, Dou, Chunmeng, Ni, Kai, Wang, Zhongrui, Shang, Dashan, Liu, Ming
Format: Conference Proceeding
Language:English
Subjects:
Autonomous systems
Digital systems
Energy consumption
Graphics processing units
In-memory computing
Robustness
Very large scale integration
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Summary: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.
ISSN:2158-9682
DOI:10.1109/VLSITechnologyandCir46769.2022.9830418