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

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:
 [1];  [1];  [1];  [1]; ORCiD logo [2];  [3];  [3];  [3]
  1. ORNL, Oak Ridge (main)
  2. Fermilab
  3. U. Tennessee, Knoxville
Publication Date:
Research Org.:
Oak Ridge National Laboratory, Oak Ridge Leadership Computing Facility (OLCF); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1452812
Report Number(s):
FERMILAB-CONF-17-659-CD
1676656
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Conference
Country of Publication:
United States
Language:
English

Citation Formats

Schuman, Catherine D., Potok, Thomas E., Young, Steven, Patton, Robert, Perdue, Gabriel, Chakma, Gangotree, Wyer, Austin, and Rose, Garrett S. Neuromorphic Computing for Temporal Scientific Data Classification. United States: N. p., 2017. Web. doi:10.1145/3183584.3183612.
Schuman, Catherine D., Potok, Thomas E., Young, Steven, Patton, Robert, Perdue, Gabriel, Chakma, Gangotree, Wyer, Austin, & Rose, Garrett S. Neuromorphic Computing for Temporal Scientific Data Classification. United States. doi:10.1145/3183584.3183612.
Schuman, Catherine D., Potok, Thomas E., Young, Steven, Patton, Robert, Perdue, Gabriel, Chakma, Gangotree, Wyer, Austin, and Rose, Garrett S. Sun . "Neuromorphic Computing for Temporal Scientific Data Classification". United States. doi:10.1145/3183584.3183612. https://www.osti.gov/servlets/purl/1452812.
@article{osti_1452812,
title = {Neuromorphic Computing for Temporal Scientific Data Classification},
author = {Schuman, Catherine D. and Potok, Thomas E. and Young, Steven and Patton, Robert and Perdue, Gabriel and Chakma, Gangotree and Wyer, Austin and Rose, Garrett S.},
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 = {2017},
month = {1}
}

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:

Works referenced in this record:

Nanoscale Memristor Device as Synapse in Neuromorphic Systems
journal, April 2010

  • Jo, Sung Hyun; Chang, Ting; Ebong, Idongesit
  • Nano Letters, Vol. 10, Issue 4, p. 1297-1301
  • DOI: 10.1021/nl904092h