Evolving Ensembles of Spiking Neural Networks for Neuromorphic Systems

Elbrecht, Daniel; Kulkarni, Shruti; Parsa, Maryam; Mitchell, Parker; Schuman, Catherine

doi:10.1109/SSCI47803.2020.9308568

Evolving Ensembles of Spiking Neural Networks for Neuromorphic Systems

Conference · Tue Dec 01 04:00:00 EST 2020

DOI:https://doi.org/10.1109/SSCI47803.2020.9308568· OSTI ID:1760126

Elbrecht, Daniel ^[1]; ^[2]; Parsa, Maryam ^[2]; Mitchell, Parker ^[2]; ^[2]

University of Central Florida
ORNL

Evolutionary algorithms have been proposed as a solution to overcome many of the challenges associated with training spiking neural networks. While evolutionary optimization for spiking neural networks is very flexible, its performance has difficulty scaling to complex tasks and correspondingly complex network structures. Here we propose a method for evolving ensembles of spiking neural networks. By using ensemble learning, the flexibility of evolutionary optimization is fully preserved while scaling to more challenging tasks. We test the performance of the proposed method using handwritten digit classification. We investigate multiple strategies for constructing ensembles of spiking neural networks, and demonstrate that evolving ensembles of SNNs offers significant performance advantages over evolutionary optimization.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

DOE Contract Number:: AC05-00OR22725

OSTI ID:: 1760126

Country of Publication:: United States

Language:: English

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