Catherine D. Schuman, Hritom Das, Garrett S. Rose and James S. Plank

July, 2024

ISVLSI: IEEE Computer Society Annual Symposium on VLSI

https://ieeexplore.ieee.org/document/10682680

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Abstract

There are a large variety of different neuron implementations that have been used in past spiking neural networks and neuromorphic hardware implementations. However, it has not been clear what the impact of neuron parameters and characteristics are on application and hardware performance. In this work, we investigate the impact of different neuronal features, including leak, and absolute and relative refractory periods, as well as their associated parameters, on application performance. We investigate the performance across a variety of real-world applications, including static and temporal classification tasks and control tasks, as well as two different algorithmic approaches, evolutionary algorithms, and reservoir computing. We analyze the impact of these neuronal features in terms of performance on the application, as well as energy usage.

Citation Information

Text

author     C. D. Schuman and H. Das and G. S. Rose and J. S. Plank
title      Evaluation of Neuron Parameters on the Performance of Spiking Neural 
           Networks and Neuromorphic Hardware
booktitle  ISVLSI: IEEE Computer Society Annual Symposium on VLSI
month      July
year       2024
publisher  IEEE
doi        10.1109/ISVLSI61997.2024.00068
url        https://ieeexplore.ieee.org/document/10682680

Bibtex

@INPROCEEDINGS{sdr:24:enp,
    author = "C. D. Schuman and H. Das and G. S. Rose and J. S. Plank",
    title = "Evaluation of Neuron Parameters on the Performance of Spiking Neural 
               Networks and Neuromorphic Hardware",
    booktitle = "ISVLSI: IEEE Computer Society Annual Symposium on VLSI",
    month = "July",
    year = "2024",
    publisher = "IEEE",
    doi = "10.1109/ISVLSI61997.2024.00068",
    url = "https://ieeexplore.ieee.org/document/10682680"
}