A Small, Low Cost Event-Driven Architecture for Spiking Neural Networks on FPGAs

J. Parker Mitchell; Catherine D. Schuman; Thomas E. Potok

doi:10.1145/3407197.3407216

A Small, Low Cost Event-Driven Architecture for Spiking Neural Networks on FPGAs

J. Parker Mitchell, Catherine D. Schuman, Thomas E. Potok

Computer Science and Mathematics Div

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

Abstract

Currently, there is a lack of availability of low cost, low power neuromorphic hardware. In this work, we introduce the μCaspian architecture along with an associated development PCB design which provides a low cost and SWaP (size, weight, and power) optimized neuromorphic hardware platform. Further, our proposed system only uses commercial off the shelf components and an open source FPGA workflow to maximize the accessibility of μCaspian to all researchers.

Original language	English
Title of host publication	ICONS 2020 - Proceedings of International Conference on Neuromorphic Systems 2020
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450388511
DOIs	https://doi.org/10.1145/3407197.3407216
State	Published - Jul 28 2020
Event	2020 International Conference on Neuromorphic Systems, ICONS 2020 - Virtual, Online, United States Duration: Jul 28 2020 → Jul 30 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2020 International Conference on Neuromorphic Systems, ICONS 2020
Country/Territory	United States
City	Virtual, Online
Period	07/28/20 → 07/30/20

Funding

Notice: This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Funders	Funder number
U.S. Department of Energy
Office of Science
Advanced Scientific Computing Research	DE-AC05-00OR22725
Oak Ridge National Laboratory

Keywords

fpga
neuromorphic
reconfigurable computing
spiking neural network

Access to Document

10.1145/3407197.3407216

Cite this

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title = "A Small, Low Cost Event-Driven Architecture for Spiking Neural Networks on FPGAs",

abstract = "Currently, there is a lack of availability of low cost, low power neuromorphic hardware. In this work, we introduce the μCaspian architecture along with an associated development PCB design which provides a low cost and SWaP (size, weight, and power) optimized neuromorphic hardware platform. Further, our proposed system only uses commercial off the shelf components and an open source FPGA workflow to maximize the accessibility of μCaspian to all researchers.",

keywords = "fpga, neuromorphic, reconfigurable computing, spiking neural network",

author = "Mitchell, {J. Parker} and Schuman, {Catherine D.} and Potok, {Thomas E.}",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 2020 International Conference on Neuromorphic Systems, ICONS 2020 ; Conference date: 28-07-2020 Through 30-07-2020",

year = "2020",

month = jul,

day = "28",

doi = "10.1145/3407197.3407216",

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Mitchell, JP, Schuman, CD & Potok, TE 2020, A Small, Low Cost Event-Driven Architecture for Spiking Neural Networks on FPGAs. in ICONS 2020 - Proceedings of International Conference on Neuromorphic Systems 2020., 3407216, ACM International Conference Proceeding Series, Association for Computing Machinery, 2020 International Conference on Neuromorphic Systems, ICONS 2020, Virtual, Online, United States, 07/28/20. https://doi.org/10.1145/3407197.3407216

A Small, Low Cost Event-Driven Architecture for Spiking Neural Networks on FPGAs. / Mitchell, J. Parker; Schuman, Catherine D.; Potok, Thomas E.
ICONS 2020 - Proceedings of International Conference on Neuromorphic Systems 2020. Association for Computing Machinery, 2020. 3407216 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Currently, there is a lack of availability of low cost, low power neuromorphic hardware. In this work, we introduce the μCaspian architecture along with an associated development PCB design which provides a low cost and SWaP (size, weight, and power) optimized neuromorphic hardware platform. Further, our proposed system only uses commercial off the shelf components and an open source FPGA workflow to maximize the accessibility of μCaspian to all researchers.

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KW - reconfigurable computing

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M3 - Conference contribution

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A Small, Low Cost Event-Driven Architecture for Spiking Neural Networks on FPGAs

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

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