Guojing Cong, Seung-Hwan Lim, Shruti Kulkarni, Prasanna Date, Thomas Potok, Shay Snyder, Maryam Parsa, and Catherine Schuman

July, 2022

Proceedings of the International Conference on Neuromorphic Systems 2022

https://dl.acm.org/doi/proceedings/10.1145/3546790

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Abstract

Graph convolutional networks have risen in popularity in recent years to tackle problems that are naturally represented as graphs. However, real-world graphs are often sparse, which means that implementing them on traditional accelerators such as graphics processing units (GPUs) can lead to inefficient utilization of the hardware. Spiking neuromorphic computers natively implement network-like computation and have been shown to be successful at implementing certain types of graph computations. In this work, we evaluate the use of a simulated network of spiking neurons to perform semi-supervised learning on graph data using only the graph structure. We demonstrate that our neuromorphic approach provides comparable results to graph convolutional network results, and we discuss the opportunities for using neuromorphic computers for this task in the future.

Citation Information

Text

author      G. Cong and S. H. Lim and S. Kulkarni and P. Date and T. Potok and S. Snyder and M. Parsa and C. D. Schuman
title       Semi-Supervised Graph Structure Learning on Neuromorphic Computers
booktitle   Proceedings of the International Conference on Neuromorphic Systems 2022
publisher   ACM
doi         10.1145/3546790.3546821
url         https://dl.acm.org/doi/abs/10.1145/3546790.3546821
year        2022

Bibtex

@INPROCEEDINGS{clk:22:ssg,
    author = "G. Cong and S. H. Lim and S. Kulkarni and P. Date and T. Potok and S. Snyder and M. Parsa and C. D. Schuman",
    title = "Semi-Supervised Graph Structure Learning on Neuromorphic Computers",
    booktitle = "Proceedings of the International Conference on Neuromorphic Systems 2022",
    publisher = "ACM",
    doi = "10.1145/3546790.3546821",
    url = "https://dl.acm.org/doi/abs/10.1145/3546790.3546821",
    year = "2022"
}