C. D. Schuman and B. Kay and P. Date and R. Kannan and P. Sao and T. E. Potok

June, 2021

IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

https://doi.org/10.1109/IPDPSW52791.2021.00054

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Abstract

Neuromorphic computers offer the opportunity for low-power, efficient computation. Though they have been primarily applied to neural network tasks, there is also the opportunity to leverage the inherent characteristics of neuromorphic computers (low power, massive parallelism, collocated processing and memory) to perform non-neural network tasks. Here, we demonstrate how an approach for performing sparse binary matrix-vector multiplication on neuromorphic computers. We describe the approach, which relies on the connection between binary matrix-vector multiplication and breadth first search, and we introduce the algorithm for performing this calculation in a neuromorphic way. We validate the approach in simulation. Finally, we provide a discussion of the runtime of this algorithm and discuss where neuromorphic computers in the future may have a computational advantage when performing this computation.

Citation Information

Text

author       C. D. Schuman and B. Kay and P. Date and R. Kannan and P. Sao and T. E. Potok
title        Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers
booktitle    IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
publisher    IEEE
pages        308-311
year         2021
url          https://doi.org/10.1109/IPDPSW52791.2021.00054
doi          10.1109/IPDPSW52791.2021.00054

Bibtex

@INPROCEEDINGS{skd:21:sbm,
    author = "C. D. Schuman and B. Kay and P. Date and R. Kannan and P. Sao and T. E. Potok",
    title = "Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers",
    booktitle = "IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)",
    publisher = "IEEE",
    pages = "308-311",
    year = "2021",
    url = "https://doi.org/10.1109/IPDPSW52791.2021.00054",
    doi = "10.1109/IPDPSW52791.2021.00054"
}