research-article

Neuromorphic computing for temporal scientific data classification

Authors:

Catherine D. Schuman,

Thomas E. Potok,

Gabriel Perdue,

Gangotree Chakma,

Garrett S. RoseAuthors Info & Claims

NCS '17: Proceedings of the Neuromorphic Computing Symposium

Article No.: 2, Pages 1 - 6

https://doi.org/10.1145/3183584.3183612

Published: 17 July 2017 Publication History

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.

References

[1]

S. Agostinelli et al. 2003. GEANT4: A Simulation toolkit. Nucl.Instrum.Meth. A506 (2003), 250--303.

[2]

Himanshu Akolkar, Cedric Meyer, Xavier Clady, Olivier Marre, Chiara Bartolozzi, Stefano Panzeri, and Ryad Benosman. 2015. What can neuromorphic event-driven precise timing add to spike-based pattern recognition? Neural computation (2015).

Digital Library

[3]

Filipp Akopyan, Jun Sawada, Andrew Cassidy, Rodrigo Alvarez-Icaza, John Arthur, Paul Merolla, Nabil Imam, Yutaka Nakamura, Pallab Datta, Gi-Joon Nam, et al. 2015. TrueNorth: Design and Tool Flow of a 65mW 1 Million Neuron Programmable Neurosynaptic Chip. (2015).

[4]

L Aliaga, L Bagby, B Baldin, A Baumbaugh, A Bodek, R Bradford, WK Brooks, D Boehnlein, S Boyd, H Budd, et al. 2014. Design, calibration, and performance of the MINERvA detector. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 743 (2014), 130--159.

[5]

Costas Andreopoulos, A Bell, D Bhattacharya, F Cavanna, J Dobson, S Dytman, H Gallagher, P Guzowski, R Hatcher, P Kehayias, et al. 2010. The GENIE neutrino monte carlo generator. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 614, 1 (2010), 87--104.

[6]

Sander M Bohte, Joost N Kok, and Han La Poutre. 2002. Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48, 1 (2002), 17--37.

[7]

Yu Chen, Gang Liu, Cheng Wang, Wenbin Zhang, Run-Wei Li, and Luxing Wang. 2014. Polymer memristor for information storage and neuromorphic applications. Materials Horizons 1, 5 (2014), 489--506.

[8]

Mark E Dean and Christopher Daffron. 2016. A VLSI Design for Neuromorphic Computing. In VLSI (ISVLSI), 2016 IEEE Computer Society Annual Symposium on. IEEE, 87--92.

[9]

Bruce Denby, Patrick Garda, Bertrand Granado, Christian Kiesling, Jean-Christophe Prévotet, and Andreas Wassatsch. 2003. Fast triggering in high-energy physics experiments using hardware neural networks. Neural Networks, IEEE Transactions on 14, 5 (2003), 1010--1027.

Digital Library

[10]

Adam Disney, John Reynolds, Catherine D Schuman, Aleksander Klibisz, Aaron Young, and James S Plank. 2016. DANNA: A neuromorphic software ecosystem. Biologically Inspired Cognitive Architectures 17 (2016), 49--56.

[11]

Margaret Drouhard, Catherine D Schuman, J Douglas Birdwell, and Mark E Dean. 2014. Visual analytics for neuroscience-inspired dynamic architectures. In Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on. IEEE, 106--113.

[12]

Victor Erokhin, Tatiana Berzina, Anteo Smerieri, Paolo Camorani, Svetlana Erokhina, and Marco P Fontana. 2010. Bio-inspired adaptive networks based on organic memristors. Nano Communication Networks 1, 2 (2010), 108--117.

[13]

Stefano Fusi. 2002. Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates. Biological cybernetics 87, 5 (2002), 459--470.

[14]

Eugene M Izhikevich. 2003. Simple model of spiking neurons. IEEE Transactions on neural networks 14, 6 (2003), 1569--1572.

Digital Library

[15]

Sung Hyun Jo, Ting Chang, Idongesit Ebong, Bhavitavya B Bhadviya, Pinaki Mazumder, and Wei Lu. 2010. Nanoscale memristor device as synapse in neuromorphic systems. Nano letters 10, 4(2010), 1297--1301.

[16]

Nikola Kasabov, Kshitij Dhoble, Nuttapod Nuntalid, and Giacomo Indiveri. 2013. Dynamic evolving spiking neural networks for on-line spatio-and spectro-temporal pattern recognition. Neural Networks 41 (2013), 188--201.

Digital Library

[17]

Dhireesha Kudithipudi, Qutaiba Saleh, Cory Merkel, James Thesing, and Bryant Wysocki. 2015. Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing. Frontiers in Neuroscience 9 (2015), 502.

[18]

Bernabé Linares-Barranco and Teresa Serrano-Gotarredona. 2009. Memristance can explain spike-time-dependent-plasticity in neural synapses. (2009).

[19]

Wolfgang Maass. 1997. Networks of spiking neurons: the third generation of neural network models. Neural networks 10, 9 (1997), 1659--1671.

[20]

Liam P Maguire, T Martin McGinnity, Brendan Glackin, Arfan Ghani, Ammar Belatreche, and Jim Harkin. 2007. Challenges for large-scale implementations of spiking neural networks on FPGAs. Neurocomputing 71, 1 (2007), 13--29.

Digital Library

[21]

Garrick Orchard, Xavier Lagorce, Christoph Posch, Steve B Furber, Ryad Benosman, and Francesco Galluppi. 2015. Real-time event-driven spiking neural network object recognition on the SpiNNaker platform. In Circuits and Systems (ISCAS), 2015 IEEE International Symposium on. IEEE, 2413--2416.

[22]

NG Pavlidis, OK Tasoulis, Vassilis P Plagianakos, G Nikiforidis, and MN Vrahatis. 2005. Spiking neural network training using evolutionary algorithms. In Neural Networks, 2005. IJCNN'05. Proceedings. 2005 IEEE International Joint Conference on, Vol. 4. IEEE, 2190--2194.

[23]

J. S. Plank, G. S. Rose, M. E. Dean, and C. D. Schuman. 2017. A CAD System for Exploring Neuromorphic Computing with Emerging Technologies. In 42nd Annual GOMACTech Conference. Reno, NV.

[24]

Anvesh Polepalli, Nicholas Soures, and Dhireesha Kudithipudi. 2016. Digital neuromorphic design of a Liquid State Machine for real-time processing. In Rebooting Computing (ICRC), IEEE International Conference on. IEEE, 1--8.

[25]

Mirko Prezioso, Farnood Merrikh-Bayat, BD Hoskins, GC Adam, Konstantin K Likharev, and Dmitri B Strukov. 2015. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 7550 (2015), 61--64.

[26]

Benjamin Schrauwen, Michiel DâĂ&Zacute;Haene, David Verstraeten, and Jan Van Campenhout. 2008. Compact hardware liquid state machines on FPGA for real-time speech recognition. Neural networks 21, 2 (2008), 511--523.

Digital Library

[27]

Catherine D Schuman, J Douglas Birdwell, and Mark E Dean. 2014. Spatiotemporal classification using neuroscience-inspired dynamic architectures. Procedia Computer Science 41 (2014), 89--97.

[28]

Catherine D Schuman, Adam Disney, Susheela P Singh, Grant Bruer, J Parker Mitchell, Aleksander Klibisz, and James S Plank. 2016. Parallel evolutionary optimization for neuromorphic network training. In Proceedings of the Workshop on Machine Learning in High Performance Computing Environments. IEEE Press, 36--46.

Digital Library

[29]

Catherine D Schuman, James S Plank, Adam Disney, and John Reynolds. 2016. An evolutionary optimization framework for neural networks and neuromorphic architectures. In Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE, 145--154.

[30]

Teresa Serrano-Gotarredona, Timothée Masquelier, Themistoklis Prodromakis, Giacomo Indiveri, and Bernabe Linares-Barranco. 2013. STDP and STDP variations with memristors for spiking neuromorphic learning systems. Frontiers in neuroscience 7 (2013), 2.

[31]

Juncheng Shen, De Ma, Zonghua Gu, Ming Zhang, Xiaolei Zhu, Xiaoqiang Xu, Qi Xu, Yangjing Shen, and Gang Pan. 2016. Darwin: a neuromorphic hardware co-processor based on Spiking Neural Networks. Science China Information Sciences (2016), 1--5.

[32]

Sen Song, Kenneth D Miller, and Larry F Abbott. 2000. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature neuroscience 3, 9 (2000), 919--926.

[33]

Adam M. Terwilliger, Gabriel N. Perdue, David Isele, Robert M. Patton, and Steven R. Young. 2017. Vertex Reconstruction of Neutrino Interactions using Deep Learning. In Neural Networks (IJCNN), 2017 International Joint Conference on. In Press.

[34]

Andres Upegui, Carlos Andrés Pena-Reyes, and Eduardo Sanchez. 2005. An FPGA platform for on-line topology exploration of spiking neural networks. Microprocessors and microsystems 29, 5 (2005), 211--223.

[35]

Salvatore Vitabile, Vincenzo Conti, Fulvio Gennaro, and Filippo Sorbello. 2005. Efficient MLP digital implementation on FPGA. In Digital System Design, 2005. Proceedings. 8th Euromicro Conference on. IEEE, 218--222.

Digital Library

[36]

Jianguo Xin and Mark J Embrechts. 2001. Supervised learning with spiking neural networks. In Neural Networks, 2001. Proceedings. IJCNN'01. International Joint Conference on, Vol. 3. IEEE, 1772--1777.

Cited By

R. Kulkarni SYoung ADate PRao Miniskar NVetter JFahim FParpillon BDickinson JTran NYoo JMills CSwartz MMaksimovic PSchuman CBean APayvand MParsa MSchuman C(2023)On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics ExperimentsProceedings of the 2023 International Conference on Neuromorphic Systems10.1145/3589737.3605976(1-8)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3589737.3605976
McClintic WScott HMoore NFarahat MMaxwell MSchuman CBolmatov DBarrera FKatsaras JCollier C(2022)Heterosynaptic plasticity in biomembrane memristors controlled by pHMRS Bulletin10.1557/s43577-022-00344-z48:1(13-21)Online publication date: 29-Aug-2022
https://doi.org/10.1557/s43577-022-00344-z
Patton RDate PKulkarni SGunaratne CLim SCong GYoung SColetti MPotok TSchuman C(2022)Neuromorphic Computing for Scientific Applications2022 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures Workshop (RSDHA)10.1109/RSDHA56811.2022.00008(22-28)Online publication date: Nov-2022
https://doi.org/10.1109/RSDHA56811.2022.00008
Show More Cited By

Index Terms

Neuromorphic computing for temporal scientific data classification

Recommendations

Effects of Noise on Leaky Integrate-and-Fire Neuron Models for Neuromorphic Computing Applications
Computational Science and Its Applications – ICCSA 2022
Abstract
Artificial neural networks (ANNs) have been extensively used for the description of problems arising from biological systems and for constructing neuromorphic computing models. The third generation of ANNs, namely, spiking neural networks (SNNs), ...
Backpropagation for energy-efficient neuromorphic computing
NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1

Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient. For the former, deep learning using ...
Memristive Spiking Neural Network for Neuromorphic Computing

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

NCS '17: Proceedings of the Neuromorphic Computing Symposium

July 2017

86 pages

ISBN:9781450364423

DOI:10.1145/3183584

Copyright © 2017 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 July 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Funding Sources

U.S. Department of Energy,

Conference

NCS '17

NCS '17: Neuromorphic Computing Symposium

July 17 - 19, 2017

Tennessee, Knoxville

Acceptance Rates

NCS '17 Paper Acceptance Rate 12 of 15 submissions, 80%;

Overall Acceptance Rate 12 of 15 submissions, 80%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
180
Total Downloads

Downloads (Last 12 months)30
Downloads (Last 6 weeks)9

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

R. Kulkarni SYoung ADate PRao Miniskar NVetter JFahim FParpillon BDickinson JTran NYoo JMills CSwartz MMaksimovic PSchuman CBean APayvand MParsa MSchuman C(2023)On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics ExperimentsProceedings of the 2023 International Conference on Neuromorphic Systems10.1145/3589737.3605976(1-8)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3589737.3605976
McClintic WScott HMoore NFarahat MMaxwell MSchuman CBolmatov DBarrera FKatsaras JCollier C(2022)Heterosynaptic plasticity in biomembrane memristors controlled by pHMRS Bulletin10.1557/s43577-022-00344-z48:1(13-21)Online publication date: 29-Aug-2022
https://doi.org/10.1557/s43577-022-00344-z
Patton RDate PKulkarni SGunaratne CLim SCong GYoung SColetti MPotok TSchuman C(2022)Neuromorphic Computing for Scientific Applications2022 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures Workshop (RSDHA)10.1109/RSDHA56811.2022.00008(22-28)Online publication date: Nov-2022
https://doi.org/10.1109/RSDHA56811.2022.00008
Schuman CPlank JRose GParsa M(2022)Lessons Learned in Omnidirectional Co-Design of Neuromorphic Systems2022 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK)10.1109/IMFEDK56875.2022.9975370(1-5)Online publication date: 28-Nov-2022
https://doi.org/10.1109/IMFEDK56875.2022.9975370
Schuman CMitchell JPatton RPotok TPlank JOkandan MAimone J(2020)Evolutionary Optimization for Neuromorphic SystemsProceedings of the 2020 Annual Neuro-Inspired Computational Elements Workshop10.1145/3381755.3381758(1-9)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3381755.3381758
Schuman CParker Mitchell JParsa MPlank JBrown SRose GPatton RPotok T(2020)Automated Design of Neuromorphic Networks for Scientific Applications at the Edge2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9207412(1-7)Online publication date: Jul-2020
https://doi.org/10.1109/IJCNN48605.2020.9207412
Plank JZhao JHurst B(2020)Reducing the Size of Spiking Convolutional Neural Networks by Trading Time for Space2020 International Conference on Rebooting Computing (ICRC)10.1109/ICRC2020.2020.00010(116-125)Online publication date: Dec-2020
https://doi.org/10.1109/ICRC2020.2020.00010
Olin-Ammentorp WCady N(2019)Biologically-Inspired Neuromorphic ComputingScience Progress10.1177/0036850419850394102:3(261-276)Online publication date: 14-May-2019
https://doi.org/10.1177/0036850419850394
Song LChen FYoung SSchuman CPerdue GPotok T(2019)Deep Learning for Vertex Reconstruction of Neutrino-nucleus Interaction Events with Combined Energy and Time DataICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2019.8683736(3882-3886)Online publication date: May-2019
https://doi.org/10.1109/ICASSP.2019.8683736
Liu JSpedalieri FYao KPotok TSchuman CYoung SPatton RRose GChamka G(2018)Adiabatic Quantum Computation Applied to Deep Learning NetworksEntropy10.3390/e2005038020:5(380)Online publication date: 18-May-2018
https://doi.org/10.3390/e20050380

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents