DANNA: A neuromorphic software ecosystem

Adam Disney, John Reynolds, Catherine D. Schuman, Aleksander Klibisz, Aaron Young, James S. Plank

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Architectures for neuromorphic computing have evolved to the point where a significant amount of software design and implementation is necessary for leveraging these architectures to solve real problems. In this paper, we describe the software ecosystem for the DANNA neuromorphic computing model. This ecosystem is composed of four pieces: a simulator, commander, evolutionary optimizer, and visualizer. The ecosystem facilitates developing applications for DANNA, and in turn will help DANNA evolve into a more effective neuromorphic computing model. We describe how we use the software components to perform digit classification using the MNIST dataset.

Original languageEnglish
Pages (from-to)49-56
Number of pages8
JournalBiologically Inspired Cognitive Architectures
Volume17
DOIs
StatePublished - Jul 1 2016
Externally publishedYes

Funding

Notice: This manuscript has been authored 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 ). This material is based on research sponsored by the Air Force Research Laboratory under agreement number FA8750-16-1-0065. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Research Laboratory or the U.S. Government.

Keywords

  • Genetic algorithms
  • Neuromorphic computing
  • Visualization

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