Adam Z. Foshie, Charles Rizzo, Hritom Das, ChaoHui Zheng, James S. Plank, and Garrett S. Rose

June, 2022

GLSVLSI - Great Lakes Symposium on VLSI

https://www.glsvlsi.org/

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Abstract

Neuromorphic computing is a leading option for non von-Neumann computing architectures. With it, neural networks are developed that derive architectural inspiration from how the brain operates with neurons, synapses, and spikes. These networks are often implemented in either software or hardware based neuroprocessors designed to handle specific tasks efficiently. Even if implemented in hardware, software emulation is instrumental in determining the worthwhile features and capabilities of the architecture. In this work two novel neuroprocessors are introduced: the software-based RISP neuroprocessor, and the RAVENS hardware neuroprocessor. Several benchmark tests using control applications are performed with each neuroprocessor configured in various ways to evaluate their comparative performance and training properties.

Citation Information

Text

author     A. Z. Foshie and C. Rizzo and H. Das and C. Zheng and J. S. Plank and G. S. Rose
title      Benchmark Comparisons of Spike-Based Reconfigurable Neuroprocessor Architectures for Control Applications
booktitle  GLSVLSI - Great Lakes Symposium on VLSI
month      June
year       2022
pages      383-386
publisher  ACM
location   Irvine, CA
url        https://doi.org/10.1145/3526241.3530381
doi        10.1145/3526241.3530381

Bibtex

@INPROCEEDINGS{frd:22:bcs,
    author = "A. Z. Foshie and C. Rizzo and H. Das and C. Zheng and J. S. Plank and G. S. Rose",
    title = "Benchmark Comparisons of Spike-Based Reconfigurable Neuroprocessor Architectures for Control Applications",
    booktitle = "GLSVLSI - Great Lakes Symposium on VLSI",
    month = "June",
    year = "2022",
    pages = "383-386",
    publisher = "ACM",
    location = "Irvine, CA",
    url = "https://doi.org/10.1145/3526241.3530381",
    doi = "10.1145/3526241.3530381"
}