Charles Rizzo, Luke McCombs, Braxton Haynie, Catherine Schuman and James S. Plank

August, 2023

ICONS: International Conference on Neuromorphic Systems

https://icons.ornl.gov/schedule/

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Abstract

Event-based cameras and classification datasets pair nicely with neuromorphic computing. Furthermore, it is attractive from a SWaP perspective to have a fully neuromorphic pipeline from event-based camera output to classification instead of having to preprocess the camera data prior to classification. In this work, we examine how two neuromorphic observation space reduction techniques impact classification performance on the DVSGesture dataset. The two techniques can be implemented as spiking neural networks so that no preprocessing of the camera data is required, and instead, only the routing of the events to the proper input neurons is necessary.

Citation Information

Text

author           C. Rizzo and L. McCombs and B. Haynie and C. D. Schuman and J. S. Plank
title            {DVSGesture} Recognition with Neuromorphic Observation Space Reduction Techniques
booktitle        International Conference on Neuromorphic Computing Systems (ICONS)
doi              10.1145/3589737.3605999
url              https://dl.acm.org/doi/10.1145/3589737.3605999
publisher        ACM
year             2023

Bibtex

@INPROCEEDINGS{rmh:23:drw,
    author = "C. Rizzo and L. McCombs and B. Haynie and C. D. Schuman and J. S. Plank",
    title = "{DVSGesture} Recognition with Neuromorphic Observation Space Reduction Techniques",
    booktitle = "International Conference on Neuromorphic Computing Systems (ICONS)",
    publisher = "ACM",
    doi = "10.1145/3589737.3605999",
    url = "https://dl.acm.org/doi/10.1145/3589737.3605999",
    year = "2023"
}