research-article

Dynamic Programming with Spiking Neural Computing

Authors:

James B. Aimone,

Cynthia A. Phillips,

William Severa,

Helen XuAuthors Info & Claims

ICONS '19: Proceedings of the International Conference on Neuromorphic Systems

Article No.: 20, Pages 1 - 9

https://doi.org/10.1145/3354265.3354285

Published: 23 July 2019 Publication History

Abstract

With the advent of large-scale neuromorphic platforms, we seek to better understand the applications of neuromorphic computing to more general-purpose computing domains. Graph analysis problems have grown increasingly relevant in the wake of readily available massive data. We demonstrate that a broad class of combinatorial and graph problems known as dynamic programs enjoy simple and efficient neuromorphic implementations, by developing a general technique to convert dynamic programs to spiking neuromorphic algorithms. Dynamic programs have been studied for over 50 years and have dozens of applications across many fields.

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Cited By

Ding XGu YSun YAgrawal KPetrank E(2024)Parallel and (Nearly) Work-Efficient Dynamic ProgrammingProceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3626183.3659958(219-232)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3626183.3659958
Luca SWang FMusuvathy S(2024)A Spiking Neuromorphic Algorithm for Markov Reward Processes2024 International Conference on Neuromorphic Systems (ICONS)10.1109/ICONS62911.2024.00060(350-357)Online publication date: 30-Jul-2024
https://doi.org/10.1109/ICONS62911.2024.00060
Theilman BAimone J(2023)Goemans-Williamson MAXCUT approximation algorithm on LoihiProceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference10.1145/3584954.3584955(1-5)Online publication date: 11-Apr-2023
https://dl.acm.org/doi/10.1145/3584954.3584955
Show More Cited By

Dynamic Programming with Spiking Neural Computing
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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cover image ACM Other conferences

ICONS '19: Proceedings of the International Conference on Neuromorphic Systems

July 2019

144 pages

ISBN:9781450376808

DOI:10.1145/3354265

Conference Chair:
Thomas E. Potok,
Program Chair:
Catherine Schuman

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGDA: ACM Special Interest Group on Design Automation
Intel: Intel

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 July 2019

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ICONS '19

ICONS '19: International Conference on Neuromorphic Systems

July 23 - 25, 2019

TN, Knoxville, USA

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Overall Acceptance Rate 13 of 22 submissions, 59%

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Cited By

Ding XGu YSun YAgrawal KPetrank E(2024)Parallel and (Nearly) Work-Efficient Dynamic ProgrammingProceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3626183.3659958(219-232)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3626183.3659958
Luca SWang FMusuvathy S(2024)A Spiking Neuromorphic Algorithm for Markov Reward Processes2024 International Conference on Neuromorphic Systems (ICONS)10.1109/ICONS62911.2024.00060(350-357)Online publication date: 30-Jul-2024
https://doi.org/10.1109/ICONS62911.2024.00060
Theilman BAimone J(2023)Goemans-Williamson MAXCUT approximation algorithm on LoihiProceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference10.1145/3584954.3584955(1-5)Online publication date: 11-Apr-2023
https://dl.acm.org/doi/10.1145/3584954.3584955
Theilman BWang YParekh OSevera WSmith JAimone J(2023)Stochastic Neuromorphic Circuits for Solving MAXCUT2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS54959.2023.00083(779-787)Online publication date: May-2023
https://doi.org/10.1109/IPDPS54959.2023.00083
Aimone JParekh O(2023)The brain’s unique take on algorithmsNature Communications10.1038/s41467-023-40535-z14:1Online publication date: 16-Aug-2023
https://doi.org/10.1038/s41467-023-40535-z
Severa WSmith JAimone JLehoucq R(2022)Learning to Parameterize a Stochastic Process Using Neuromorphic Data GenerationProceedings of the International Conference on Neuromorphic Systems 202210.1145/3546790.3546797(1-7)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1145/3546790.3546797
Vineyard CCardwell SChance FMusuvathy SRothganger FSevera WSmith JTeeter CWang FAimone J(2022)Neural Mini-Apps as a Tool for Neuromorphic Computing InsightProceedings of the 2022 Annual Neuro-Inspired Computational Elements Conference10.1145/3517343.3517353(40-49)Online publication date: 28-Mar-2022
https://dl.acm.org/doi/10.1145/3517343.3517353
Kulvicius THerzog STamosiunaite MWorgotter F(2022)Finding Optimal Paths Using Networks Without Learning—Unifying Classical ApproachesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.308902333:12(7877-7887)Online publication date: Dec-2022
https://doi.org/10.1109/TNNLS.2021.3089023
Smith JHill AReeder LFranke BLehoucq RParekh OSevera WAimone J(2022)Neuromorphic scaling advantages for energy-efficient random walk computationsNature Electronics10.1038/s41928-021-00705-75:2(102-112)Online publication date: 14-Feb-2022
https://doi.org/10.1038/s41928-021-00705-7
Powell HWinkel MHopp ALinde H(2022)A hybrid biological neural network model for solving problems in cognitive planningScientific Reports10.1038/s41598-022-11567-012:1Online publication date: 23-Jun-2022
https://doi.org/10.1038/s41598-022-11567-0
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