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 language | English |
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Title of host publication | GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion |
Publisher | Association for Computing Machinery, Inc |
Pages | 306-307 |
Number of pages | 2 |
ISBN (Electronic) | 9781450367486 |
DOIs | |
State | Published - Jul 13 2019 |
Event | 2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic Duration: Jul 13 2019 → Jul 17 2019 |
Publication series
Name | GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion |
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Conference
Conference | 2019 Genetic and Evolutionary Computation Conference, GECCO 2019 |
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Country/Territory | Czech Republic |
City | Prague |
Period | 07/13/19 → 07/17/19 |
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