Catherine D. Schuman, J. Parker Mitchell, Maryam Parsa, James S. Plank, Samuel D. Brown, Garrett S. Rose, Robert M. Patton and Thomas E. Potok

July, 2020

IJCNN: The International Joint Conference on Neural Networks

https://www.ijcnn.org/

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Abstract

Designing spiking neural networks for neuromorphic deployment is a non-trivial task. It is further complicated when there are resource constraints for the neuromorphic implementation, such as size or power constraints, that may be present in edge applications. In this work, we utilize a previously presented approach, EONS, to design spiking neural networks for a memristive neuromorphic implementation for scientific data applications. We specifically use a multi-objective approach in EONS to maximize network accuracy on the scientific data application task, but also to minimize network size and energy. We illustrate that EONS determines both the network structure and the parameters, removing the burden from the user on determining the appropriate spiking neural network structure, and we show that the resulting networks are very different from the layered structure of typical neural networks. Finally, we show that the multi-objective approach produces smaller, more energy efficient networks than the original EONS approach and produces comparable accuracy to a back-propagation style training approach.

Citation Information

Text

author    C. D. Schuman and J. P. Mitchell and M. Parsa and J. S. Plank and S. D. Brown and
          G. S. Rose and R. M. Patton and T. E. Potok
title     Automated Design of Neuromorphic Networks for Scientific Applications at the Edge
booktitle IJCNN: The International Joint Conference on Neural Networks
year      2020

Bibtex

@INPROCEEDINGS{smp:20:adn,
    author = "C. D. Schuman and J. P. Mitchell and M. Parsa and J. S. Plank and S. D. Brown and
                G. S. Rose and R. M. Patton and T. E. Potok",
    title = "Automated Design of Neuromorphic Networks for Scientific Applications at the Edge",
    booktitle = "IJCNN: The International Joint Conference on Neural Networks",
    year = "2020"
}