Island model for parallel evolutionary optimization of spiking neuromorphic computing

Catherine D. Schuman, James S. Plank, Robert M. Patton, Thomas E. Potok

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Parallel genetic algorithms (PGAs) can be used to accelerate optimization by exploiting large-scale computational resources. In this work, we describe a PGA framework for evolving spiking neural networks (SNNs) for neuromorphic hardware implementation. The PGA framework is based on an islands model with migration. We show that using this framework, better SNNs for neuromorphic systems can be evolved faster.

Original languageEnglish
Title of host publicationGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages306-307
Number of pages2
ISBN (Electronic)9781450367486
DOIs
StatePublished - Jul 13 2019
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: Jul 13 2019Jul 17 2019

Publication series

NameGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Country/TerritoryCzech Republic
CityPrague
Period07/13/1907/17/19

Funding

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DE-AC05-00OR22725 and by an Air Force Research Laboratory Information Directorate grant (FA8750-16-1-0065). This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.

Keywords

  • Evolutionary optimization
  • Island models
  • Neuromorphic computing
  • Spiking neural networks

Fingerprint

Dive into the research topics of 'Island model for parallel evolutionary optimization of spiking neuromorphic computing'. Together they form a unique fingerprint.

Cite this