Abstract
Designing and training an appropriate spiking neural network for neuromorphic deployment remains an open challenge in neuromorphic computing. In 2016, we introduced an approach for utilizing evolutionary optimization to address this challenge called Evolutionary Optimization for Neuromorphic Systems (EONS). In this work, we present an improvement to this approach that enables rapid prototyping of new applications of spiking neural networks in neuromorphic systems. We discuss the overall EONS framework and its improvements over the previous implementation. We present several case studies of how EONS can be used, including to train spiking neural networks for classification and control tasks, to train under hardware constraints, to evolve a reservoir for a liquid state machine, and to evolve smaller networks using multi-objective optimization.
| Original language | English |
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| Title of host publication | Proceedings of the 2020 Annual Neuro-Inspired Computational Elements Workshop, NICE 2020 |
| Publisher | Association for Computing Machinery |
| ISBN (Electronic) | 9781450361231 |
| DOIs | |
| State | Published - Mar 17 2020 |
| Event | 2020 Annual Neuro-Inspired Computational Elements Workshop, NICE 2020 - Heidelberg, Germany Duration: Mar 17 2020 → Mar 20 2020 |
Publication series
| Name | ACM International Conference Proceeding Series |
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Conference
| Conference | 2020 Annual Neuro-Inspired Computational Elements Workshop, NICE 2020 |
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| Country/Territory | Germany |
| City | Heidelberg |
| Period | 03/17/20 → 03/20/20 |
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 the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory. Notice: This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- genetic algorithms
- neuromorphic computing
- spiking neural networks