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Evaluating Encoding and Decoding Approaches for Spiking Neuromorphic Systems

Authors:

Catherine Schuman,

John McDonald-Carmack,

Nicholas Skuda,

James PlankAuthors Info & Claims

ICONS '22: Proceedings of the International Conference on Neuromorphic Systems 2022

Article No.: 2, Pages 1 - 9

https://doi.org/10.1145/3546790.3546792

Published: 07 September 2022 Publication History

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Abstract

A challenge associated with effectively using spiking neuromorphic systems is how to communicate data to and from the neuromorphic implementation. Unless a neuromorphic or event-based sensing system is used, data has to be converted into spikes to be processed as input by the neuromorphic system. The output spikes produced by the neuromorphic system have to be turned back into a value or decision. There are a variety of commonly used input encoding approaches, such as rate coding, temporal coding, and population coding, as well as several commonly used output approaches, such as voting or first-to-spike. However, it is not clear which is the most appropriate approach to use or whether the choice of encoding or decoding approach has a significant impact on performance. In this work, we evaluate the performance of several encoding and decoding approaches on classification, regression, and control tasks. We show that the choice of encoding and decoding approaches significantly impact performance on these tasks, and we make recommendations on how to select the appropriate encoding and decoding approaches for real-world applications.

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Cited By

Nilsson MSchelén OLindgren ABodin UPaniagua CDelsing JSandin F(2023)Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directionsFrontiers in Neuroscience10.3389/fnins.2023.107443917Online publication date: 17-Feb-2023
https://doi.org/10.3389/fnins.2023.1074439
Akl MErgene DWalter FKnoll A(2023)Toward robust and scalable deep spiking reinforcement learningFrontiers in Neurorobotics10.3389/fnbot.2022.107564716Online publication date: 20-Jan-2023
https://doi.org/10.3389/fnbot.2022.1075647
Severa WCardwell SKrygier MRothganger FVineyard CPayvand MParsa MSchuman C(2023)Neuromorphic Population Evaluation using the Fugu FrameworkProceedings of the 2023 International Conference on Neuromorphic Systems10.1145/3589737.3605992(1-7)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3589737.3605992
Show More Cited By

Index Terms

Evaluating Encoding and Decoding Approaches for Spiking Neuromorphic Systems
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Other conferences

ICONS '22: Proceedings of the International Conference on Neuromorphic Systems 2022

July 2022

213 pages

ISBN:9781450397896

DOI:10.1145/3546790

Editors:
Thomas E. Potok
Oak Ridge National Laboratory
,
Catherine Schuman
University of Tennessee Knoxville
,
Melika Payvand
University of Zurich
,
Prasanna Date
Oak Ridge National Laboratory
,
Shruti Kulkarni
Oak Ridge National Laboratory
,
Yiran Chen
Duke University
,
Robinson Pino
U.S. Department of Energy
,
Brad Aimone
Sandia National Laboratories
,
Mutsumi Kimura
Ryukoku University
,
Gregory Cohen
Western Sydney University
,
David Whittaker
Elm Street Ventures
,
Gordon Hirsch Wilson
Rain Neuromorphics

Copyright © 2022 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2022

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Refereed limited

Funding Sources

Air Force Research Laboratory
Department of Energy

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ICONS

ICONS: International Conference on Neuromorphic Systems

July 27 - 29, 2022

TN, Knoxville, USA

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Overall Acceptance Rate 13 of 22 submissions, 59%

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Cited By

Nilsson MSchelén OLindgren ABodin UPaniagua CDelsing JSandin F(2023)Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directionsFrontiers in Neuroscience10.3389/fnins.2023.107443917Online publication date: 17-Feb-2023
https://doi.org/10.3389/fnins.2023.1074439
Akl MErgene DWalter FKnoll A(2023)Toward robust and scalable deep spiking reinforcement learningFrontiers in Neurorobotics10.3389/fnbot.2022.107564716Online publication date: 20-Jan-2023
https://doi.org/10.3389/fnbot.2022.1075647
Severa WCardwell SKrygier MRothganger FVineyard CPayvand MParsa MSchuman C(2023)Neuromorphic Population Evaluation using the Fugu FrameworkProceedings of the 2023 International Conference on Neuromorphic Systems10.1145/3589737.3605992(1-7)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3589737.3605992
Schuman CDas HPlank JAziz ARose G(2023)Evaluating Neuron Models through Application-Hardware Co-Design2023 57th Asilomar Conference on Signals, Systems, and Computers10.1109/IEEECONF59524.2023.10477027(537-542)Online publication date: 29-Oct-2023
https://doi.org/10.1109/IEEECONF59524.2023.10477027
Wurm ASeay RDate PKulkarni SYoung AVetter J(2023)Arithmetic Primitives for Efficient Neuromorphic Computing2023 IEEE International Conference on Rebooting Computing (ICRC)10.1109/ICRC60800.2023.10386397(1-5)Online publication date: 5-Dec-2023
https://doi.org/10.1109/ICRC60800.2023.10386397

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