skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Island model for parallel evolutionary optimization of spiking neuromorphic computing

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.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
  2. University of Tennessee (UT)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1546518
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: The Genetic and Evolutionary Computation Conference (GECCO 2019) - Prague, , Czech Republic - 7/13/2019 8:00:00 AM-7/17/2019 8:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Schuman, Catherine D., Plank, James, Patton, Robert M., and Potok, Thomas E. Island model for parallel evolutionary optimization of spiking neuromorphic computing. United States: N. p., 2019. Web. doi:10.1145/3319619.3322016.
Schuman, Catherine D., Plank, James, Patton, Robert M., & Potok, Thomas E. Island model for parallel evolutionary optimization of spiking neuromorphic computing. United States. doi:10.1145/3319619.3322016.
Schuman, Catherine D., Plank, James, Patton, Robert M., and Potok, Thomas E. Mon . "Island model for parallel evolutionary optimization of spiking neuromorphic computing". United States. doi:10.1145/3319619.3322016. https://www.osti.gov/servlets/purl/1546518.
@article{osti_1546518,
title = {Island model for parallel evolutionary optimization of spiking neuromorphic computing},
author = {Schuman, Catherine D. and Plank, James and Patton, Robert M. and Potok, Thomas E.},
abstractNote = {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.},
doi = {10.1145/3319619.3322016},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {7}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: