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- W4286571889 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." @default.
- W4286571889 created "2022-07-22" @default.
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- W4286571889 date "2022-06-12" @default.
- W4286571889 modified "2023-10-17" @default.
- W4286571889 title "Few-shot graph learning with robust and energy-efficient memory-augmented graph neural network (MAGNN) based on homogeneous computing-in-memory" @default.
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- W4286571889 doi "https://doi.org/10.1109/vlsitechnologyandcir46769.2022.9830418" @default.
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