research-article

Computational Complexity of Neuromorphic Algorithms

Authors:

Catherine Schuman,

Thomas PotokAuthors Info & Claims

ICONS 2021: International Conference on Neuromorphic Systems 2021

Article No.: 8, Pages 1 - 7

https://doi.org/10.1145/3477145.3477154

Published: 13 October 2021 Publication History

Abstract

Neuromorphic computing has several characteristics that make it an extremely compelling computing paradigm for post Moore computation. Some of these characteristics include intrinsic parallelism, inherent scalability, collocated processing and memory, and event-driven computation. While these characteristics impart energy efficiency to neuromorphic systems, they do come with their own set of challenges. One of the biggest challenges in neuromorphic computing is to establish the theoretical underpinnings of the computational complexity of neuromorphic algorithms. In this paper, we take the first steps towards defining the space and time complexity of neuromorphic algorithms. Specifically, we describe a model of neuromorphic computation and state the assumptions that govern the computational complexity of neuromorphic algorithms. Next, we present a theoretical framework to define the computational complexity of a neuromorphic algorithm. We explicitly define what space and time complexities mean in the context of neuromorphic algorithms based on our model of neuromorphic computation. Finally, we leverage our approach and define the computational complexities of six neuromorphic algorithms: constant function, successor function, predecessor function, projection function, neuromorphic sorting algorithm and neighborhood subgraph extraction algorithm.

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Cited By

Raikar AAndrew JDessai PPrabhu SJathar SPrabhu ANaik MRaikar G(2024)Neuromorphic computing for modeling neurological and psychiatric disorders: implications for drug developmentArtificial Intelligence Review10.1007/s10462-024-10948-357:12Online publication date: 10-Oct-2024
https://doi.org/10.1007/s10462-024-10948-3
Vetter JDate PFahim FKulkarni SMaksymovych PTalin ATallada MVanna-iampikul PYoung ABrooks DCao YGu-Yeon WLim SLiu FMarinella MSumpter BMiniskar N(2023)Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materialsThe International Journal of High Performance Computing Applications10.1177/1094342023117853737:3-4(351-379)Online publication date: 22-Jun-2023
https://doi.org/10.1177/10943420231178537
Maheshwari DYoung ADate PKulkarni SWitherspoon BMiniskar N(2023)An FPGA-Based Neuromorphic Processor with All-to-All Connectivity2023 IEEE International Conference on Rebooting Computing (ICRC)10.1109/ICRC60800.2023.10386808(1-5)Online publication date: 5-Dec-2023
https://doi.org/10.1109/ICRC60800.2023.10386808
Show More Cited By

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Published In

cover image ACM Other conferences

ICONS 2021: International Conference on Neuromorphic Systems 2021

July 2021

198 pages

ISBN:9781450386913

DOI:10.1145/3477145

Editors:
Thomas E. Potok
Oak Ridge National Laboratory
,
Melika Payvand
ETH Zurich
,
Catherine Schuman
Oak Ridge National Laboratory
,
Prasanna Date
Oak Ridge National Laboratory
,
Mutsumi Kimura
Nara Institute of Science and Technology
,
Cory Merkel
Rochester Institute of Technology
,
Brad Aimone
Sandia National Laboratories
,
Sonia Buckley
National Institute of Standards and Technology
,
Yiran Chen
Duke University
,
Gregory Cohen
Western Sydney University
,
Todd Hylton
University of California, San Diego
,
Mutsumi Kimura
Ryukoku University, Japan
,
Robert Patton
Oak Ridge National Laboratory
,
Melika Payvand
ETH Zurich
,
Robinson Pino
U.S. Department of Energy
,
Thomas Potok
Oak Ridge National Laboratory
,
Garrett Rose
University of Tennessee, Knoxville

Copyright © 2021 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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New York, NY, United States

Publication History

Published: 13 October 2021

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ICONS 2021

ICONS 2021: International Conference on Neuromorphic Systems 2021

July 27 - 29, 2021

TN, Knoxville, USA

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Overall Acceptance Rate 13 of 22 submissions, 59%

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Cited By

Raikar AAndrew JDessai PPrabhu SJathar SPrabhu ANaik MRaikar G(2024)Neuromorphic computing for modeling neurological and psychiatric disorders: implications for drug developmentArtificial Intelligence Review10.1007/s10462-024-10948-357:12Online publication date: 10-Oct-2024
https://doi.org/10.1007/s10462-024-10948-3
Vetter JDate PFahim FKulkarni SMaksymovych PTalin ATallada MVanna-iampikul PYoung ABrooks DCao YGu-Yeon WLim SLiu FMarinella MSumpter BMiniskar N(2023)Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materialsThe International Journal of High Performance Computing Applications10.1177/1094342023117853737:3-4(351-379)Online publication date: 22-Jun-2023
https://doi.org/10.1177/10943420231178537
Maheshwari DYoung ADate PKulkarni SWitherspoon BMiniskar N(2023)An FPGA-Based Neuromorphic Processor with All-to-All Connectivity2023 IEEE International Conference on Rebooting Computing (ICRC)10.1109/ICRC60800.2023.10386808(1-5)Online publication date: 5-Dec-2023
https://doi.org/10.1109/ICRC60800.2023.10386808
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
Date PKulkarni SYoung ASchuman CPotok TVetter J(2023)Encoding integers and rationals on neuromorphic computers using virtual neuronScientific Reports10.1038/s41598-023-35005-x13:1Online publication date: 6-Jul-2023
https://doi.org/10.1038/s41598-023-35005-x
Patton RDate PKulkarni SGunaratne CLim SCong GYoung SColetti MPotok TSchuman C(2022)Neuromorphic Computing for Scientific Applications2022 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures Workshop (RSDHA)10.1109/RSDHA56811.2022.00008(22-28)Online publication date: Nov-2022
https://doi.org/10.1109/RSDHA56811.2022.00008
Date PKulkarni SYoung ASchuman CPotok TVetter J(2022)Virtual Neuron: A Neuromorphic Approach for Encoding Numbers2022 IEEE International Conference on Rebooting Computing (ICRC)10.1109/ICRC57508.2022.00017(100-105)Online publication date: Dec-2022
https://doi.org/10.1109/ICRC57508.2022.00017

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