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Title: Neuromorphic computing for temporal scientific data classification

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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2];  [3];  [4];  [3]
  1. ORNL
  2. Fermi National Accelerator Laboratory (FNAL)
  3. University of Tennessee (UT)
  4. The University of Tennessee, Knoxville
Publication Date:
Research Org.:
Oak Ridge National Laboratory, Oak Ridge Leadership Computing Facility (OLCF); 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:
1435238
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: Neuromorphic Computing Symposium - Knoxvvile, Tennessee, United States of America - 7/17/2017 12:00:00 PM-7/19/2017 12:00:00 PM
Country of Publication:
United States
Language:
English

Citation Formats

Schuman, Catherine D., Potok, Thomas E., Young, Steven R., Patton, Robert M., Perdue, Gabriel, Chakma, Gangotree, Wyer, Austin, and Rose, Garrett. Neuromorphic computing for temporal scientific data classification. United States: N. p., 2018. Web. doi:10.1145/3183584.3183612.
Schuman, Catherine D., Potok, Thomas E., Young, Steven R., Patton, Robert M., Perdue, Gabriel, Chakma, Gangotree, Wyer, Austin, & Rose, Garrett. Neuromorphic computing for temporal scientific data classification. United States. doi:10.1145/3183584.3183612.
Schuman, Catherine D., Potok, Thomas E., Young, Steven R., Patton, Robert M., Perdue, Gabriel, Chakma, Gangotree, Wyer, Austin, and Rose, Garrett. Sun . "Neuromorphic computing for temporal scientific data classification". United States. doi:10.1145/3183584.3183612. https://www.osti.gov/servlets/purl/1435238.
@article{osti_1435238,
title = {Neuromorphic computing for temporal scientific data classification},
author = {Schuman, Catherine D. and Potok, Thomas E. and Young, Steven R. and Patton, Robert M. and Perdue, Gabriel and Chakma, Gangotree and Wyer, Austin and Rose, Garrett},
abstractNote = {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.},
doi = {10.1145/3183584.3183612},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {4}
}

Conference:
Other availability
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