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Reconfigurable analogue hardware evolution of adaptive spiking neural network controllers

Authors:

Brian McGinley,

Patrick Rocke,

Fearghal Morgan,

John MaherAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 289 - 290

https://doi.org/10.1145/1389095.1389143

Published: 12 July 2008 Publication History

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Abstract

This paper details the hardware evolution of adaptive Spiking Neural Network (SNN) controllers, implemented on a network of cascaded Field Programmable Analogue Arrays (FPAAs). The fixed architecture, feed forward SNNs are trained using a Genetic Algorithm (GA). An obstacle avoidance simulated robotics controller application is chosen to test the FPAA reconfigurable hardware evolution platform. Evolved behaviours, resulting from FPAA-based SNN controllers, are compared with those obtained using software-based SNN implementations. Results presented indicate the emergence of effective behaviours and adaptation to environmental change.

References

[1]

Anadigm Field Programmable Analog Arrays. www.anadigm.com.

Google Scholar

[2]

D. Berenson, N. Estevez, and H. Lipson. Hardware Evolution of Analog Circuits for Insitu Robotic Fault-Recovery. Evolvable Hardware, 2:12--19.

Digital Library

Google Scholar

[3]

W. Gerstner and W.M. Kistler. Spiking neuron models. 2002.

Google Scholar

[4]

J. Hereford and C. Pruitt. Robust sensor systems using evolvable hardware. Evolvable Hardware, 2004. Proceedings. 2004 NASA/DoD Conference on, pages 161--168, 2004.

Crossref

Google Scholar

[5]

J. Maher, B. Mc Ginley, P. Rocke, and F. Morgan. Intrinsic Hardware Evolution of Neural Networks in Reconfigurable Analogue and Digital Devices. Proceedings of the 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM'06)-Volume 00, pages 321--322, 2006.

Digital Library

Google Scholar

[6]

S. Nolfi, D. Floreano, O. Miglino, and F. Mondada. How to evolve autonomous robots: Different approaches in evolutionary robotics. Artificial Life IV, pages 190--197, 1994.

Google Scholar

[7]

J. Plante, H. Shaw, L. Mickens, and C. Johnson-Bey. Overview of field programmable analog arrays as enabling technology for evolvable hardware for high reliability systems. Evolvable Hardware, 2003. Proceedings. NASA/DoD Conference on, pages 77--78, 2003.

Crossref

Google Scholar

[8]

P. Rocke, J. Maher, and F. Morgan. Platform for Intrinsic Evolution of Analogue Neural Networks. Proceedings of the 2005 International Conference on Reconfigurable Computing and FPGAs (ReConFig'05) on Reconfigurable Computing and FPGAs, 2005.

Digital Library

Google Scholar

[9]

M.A. Terry, J. Marcus, M. Farrell, V. Aggarwal, and U.M. O'Reilly. GRACE: Generative Robust Analog Circuit Exploration. 9th European Conference on Genetic Programming, EVO-Workshops, EVOHOT track, 2006.

Digital Library

Google Scholar

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Shrestha AFang HMei ZRider DWu QQiu Q(2022)A Survey on Neuromorphic Computing: Models and HardwareIEEE Circuits and Systems Magazine10.1109/MCAS.2022.316633122:2(6-35)Online publication date: Oct-2023
https://doi.org/10.1109/MCAS.2022.3166331
Moreno DBarrio AJuan G(2020)Simulating and deploying analog arithmetic circuits on FPAASProceedings of the 2020 Summer Simulation Conference10.5555/3427510.3427530(1-12)Online publication date: 20-Jul-2020
https://dl.acm.org/doi/10.5555/3427510.3427530
Pilat A(2015)Study on Artificial Neuron Embedded into the Dynamically Programmable Analogue Signal ProcessorIFAC-PapersOnLine10.1016/j.ifacol.2015.07.07748:4(454-459)Online publication date: 2015
https://doi.org/10.1016/j.ifacol.2015.07.077
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Index Terms

Reconfigurable analogue hardware evolution of adaptive spiking neural network controllers
1. Hardware
  1. Hardware test
  2. Robustness

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Shrestha AFang HMei ZRider DWu QQiu Q(2022)A Survey on Neuromorphic Computing: Models and HardwareIEEE Circuits and Systems Magazine10.1109/MCAS.2022.316633122:2(6-35)Online publication date: Oct-2023
https://doi.org/10.1109/MCAS.2022.3166331
Moreno DBarrio AJuan G(2020)Simulating and deploying analog arithmetic circuits on FPAASProceedings of the 2020 Summer Simulation Conference10.5555/3427510.3427530(1-12)Online publication date: 20-Jul-2020
https://dl.acm.org/doi/10.5555/3427510.3427530
Pilat A(2015)Study on Artificial Neuron Embedded into the Dynamically Programmable Analogue Signal ProcessorIFAC-PapersOnLine10.1016/j.ifacol.2015.07.07748:4(454-459)Online publication date: 2015
https://doi.org/10.1016/j.ifacol.2015.07.077
Morgan FCawley SMc Ginley BPande SMc Daid LGlackin BMaher JHarkin J(2009)Exploring the evolution of NoC-based Spiking Neural Networks on FPGAs2009 International Conference on Field-Programmable Technology10.1109/FPT.2009.5377663(300-303)Online publication date: Dec-2009
https://doi.org/10.1109/FPT.2009.5377663

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Investigating the Suitability of FPAAs for Evolved Hardware Spiking Neural Networks

Reconfigurable hardware evolution platform for a spiking neural network robotics controller

A Hardware Architecture for Image Clustering Using Spiking Neural Networks

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