Title
Neuromorphic computing for temporal scientific data classification
Date Issued
2017
Access level
restricted access
Resource Type
conference paper
Author(s)
Publisher(s)
Association for Computing Machinery
Abstract
In this work, we apply a spiking neural network model and an associated memristive neuromorphic implementation to an application in classifying temporal scientific data. We demonstrate that the spiking neural network model achieves comparable results to a previously reported convolutional neural network model, with significantly fewer neurons and synapses required. © 2017 Association for Computing Machinery.
Volume
2017-July
Number
3
Language
English
Subjects
Scopus EID
2-s2.0-85047005652
Source
ACM International Conference Proceeding Series
ISBN of the container
9781450364423
Conference
2017 Neuromorphic Computing Symposium, NCS 2017
Sponsor(s)
∗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, worldwide 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).
Sources of information:
Directorio de Producción Científica