Evolutionary Optimization for Neuromorphic Systems

Schuman, Catherine; Mitchell, Parker; Patton, Robert; Potok, Thomas; Plank, James

doi:10.1145/3381755.3381758

Evolutionary Optimization for Neuromorphic Systems

Conference · Sun Mar 01 04:00:00 EST 2020

DOI:https://doi.org/10.1145/3381755.3381758· OSTI ID:1649325

^[1]; Mitchell, Parker ^[1]; ^[1]; ^[1]; Plank, James ^[2]

ORNL
University of Tennessee (UT)

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.

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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:: 1649325

Country of Publication:: United States

Language:: English

References (14)

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