Sparse Hardware Embedding of Spiking Neuron Systems for Community Detection
Abstract
We study the applicability of spiking neural networks and neuromorphic hardware for solving general optimization problems without the use of adaptive training or learning algorithms. We leverage the dynamics of Hopfield networks and spin-glass systems to construct a fully connected spiking neural system to generate synchronous spike responses indicative of the underlying community structure in an undirected, unweighted graph. Mapping this fully connected system to current generation neuromorphic hardware is done by embedding sparse tree graphs to generate only the leading-order spiking dynamics. Here, we demonstrate that for a chosen set of benchmark graphs, the spike responses generated on a current generation neuromorphic processor can improve the stability of graph partitions and non-overlapping communities can be identified even with the loss of higher-order spiking behavior if the graphs are sufficiently dense. For sparse graphs, the loss of higher-order spiking behavior improves the stability of certain graph partitions but does not retrieve the known community memberships.
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1504017
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- ACM Journal on Emerging Technologies in Computing Systems
- Additional Journal Information:
- Journal Volume: 14; Journal Issue: 4; Journal ID: ISSN 1550-4832
- Publisher:
- Association for Computing Machinery
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; optimization; community detection; neural network; graph algorithm
Citation Formats
Hamilton, Kathleen E., Imam, Neena, and Humble, Travis S. Sparse Hardware Embedding of Spiking Neuron Systems for Community Detection. United States: N. p., 2018.
Web. doi:10.1145/3223048.
Hamilton, Kathleen E., Imam, Neena, & Humble, Travis S. Sparse Hardware Embedding of Spiking Neuron Systems for Community Detection. United States. https://doi.org/10.1145/3223048
Hamilton, Kathleen E., Imam, Neena, and Humble, Travis S. 2018.
"Sparse Hardware Embedding of Spiking Neuron Systems for Community Detection". United States. https://doi.org/10.1145/3223048. https://www.osti.gov/servlets/purl/1504017.
@article{osti_1504017,
title = {Sparse Hardware Embedding of Spiking Neuron Systems for Community Detection},
author = {Hamilton, Kathleen E. and Imam, Neena and Humble, Travis S.},
abstractNote = {We study the applicability of spiking neural networks and neuromorphic hardware for solving general optimization problems without the use of adaptive training or learning algorithms. We leverage the dynamics of Hopfield networks and spin-glass systems to construct a fully connected spiking neural system to generate synchronous spike responses indicative of the underlying community structure in an undirected, unweighted graph. Mapping this fully connected system to current generation neuromorphic hardware is done by embedding sparse tree graphs to generate only the leading-order spiking dynamics. Here, we demonstrate that for a chosen set of benchmark graphs, the spike responses generated on a current generation neuromorphic processor can improve the stability of graph partitions and non-overlapping communities can be identified even with the loss of higher-order spiking behavior if the graphs are sufficiently dense. For sparse graphs, the loss of higher-order spiking behavior improves the stability of certain graph partitions but does not retrieve the known community memberships.},
doi = {10.1145/3223048},
url = {https://www.osti.gov/biblio/1504017},
journal = {ACM Journal on Emerging Technologies in Computing Systems},
issn = {1550-4832},
number = 4,
volume = 14,
place = {United States},
year = {Thu Nov 01 00:00:00 EDT 2018},
month = {Thu Nov 01 00:00:00 EDT 2018}
}
Works referenced in this record:
Neural networks and physical systems with emergent collective computational abilities.
journal, April 1982
- Hopfield, J. J.
- Proceedings of the National Academy of Sciences, Vol. 79, Issue 8
Mapping Generative Models onto a Network of Digital Spiking Neurons
journal, August 2016
- Pedroni, Bruno U.; Das, Srinjoy; Arthur, John V.
- IEEE Transactions on Biomedical Circuits and Systems, Vol. 10, Issue 4
Spike-Train Communities: Finding Groups of Similar Spike Trains
journal, February 2011
- Humphries, M. D.
- Journal of Neuroscience, Vol. 31, Issue 6
Gibbs sampling with low-power spiking digital neurons
conference, May 2015
- Das, Srinjoy; Pedroni, Bruno Umbria; Merolla, Paul
- 2015 IEEE International Symposium on Circuits and Systems (ISCAS)
Community detection with spiking neural networks for neuromorphic hardware
conference, January 2017
- Hamilton, Kathleen E.; Imam, Neena; Humble, Travis S.
- Proceedings of the Neuromorphic Computing Symposium on - NCS '17
Convolutional networks for fast, energy-efficient neuromorphic computing
journal, September 2016
- Esser, Steven K.; Merolla, Paul A.; Arthur, John V.
- Proceedings of the National Academy of Sciences, Vol. 113, Issue 41
Multiresolution community detection for megascale networks by information-based replica correlations
journal, July 2009
- Ronhovde, Peter; Nussinov, Zohar
- Physical Review E, Vol. 80, Issue 1
Local resolution-limit-free Potts model for community detection
journal, April 2010
- Ronhovde, Peter; Nussinov, Zohar
- Physical Review E, Vol. 81, Issue 4
Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores
conference, August 2013
- Cassidy, Andrew S.; Merolla, Paul; Arthur, John V.
- 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas), The 2013 International Joint Conference on Neural Networks (IJCNN)
A million spiking-neuron integrated circuit with a scalable communication network and interface
journal, August 2014
- Merolla, P. A.; Arthur, J. V.; Alvarez-Icaza, R.
- Science, Vol. 345, Issue 6197
Brian: a simulator for spiking neural networks in Python
journal, January 2008
- Goodman, Dan
- Frontiers in Neuroinformatics, Vol. 2
Selection of intrinsic horizontal connections in the visual cortex by correlated neuronal activity
journal, January 1992
- Lowel, S.; Singer, W.
- Science, Vol. 255, Issue 5041