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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2012 - 11

 HIGHLY CITED PAPER 

Optimized Simulation Framework for Spiking Neural Networks using GPU's

MIRSU, R. See more information about MIRSU, R. on SCOPUS See more information about MIRSU, R. on IEEExplore See more information about MIRSU, R. on Web of Science, MICUT, S. See more information about  MICUT, S. on SCOPUS See more information about  MICUT, S. on SCOPUS See more information about MICUT, S. on Web of Science, CALEANU, C. See more information about  CALEANU, C. on SCOPUS See more information about  CALEANU, C. on SCOPUS See more information about CALEANU, C. on Web of Science, MIRSU, D. B. See more information about MIRSU, D. B. on SCOPUS See more information about MIRSU, D. B. on SCOPUS See more information about MIRSU, D. B. on Web of Science
 
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Download PDF pdficon (648 KB) | Citation | Downloads: 971 | Views: 4,294

Author keywords
artificial intelligence, biological neural networks, GPU computing, parallel processing, spiking neural networks

References keywords
neural(15), spiking(12), networks(10), neurons(7), model(7), tiponut(4), network(4), mirsu(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-05-30
Volume 12, Issue 2, Year 2012, On page(s): 61 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.02011
Web of Science Accession Number: 000305608000011
SCOPUS ID: 84865306303

Abstract
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This paper presents a hardware accelerated model of a spiking neural network implemented in CUDA C. It does a short description of the mathematical model for the neural network and presents the implementation on the GPU. Additionally, it presents three methods of further accelerating the model by eliminating excess kernel launch overhead time, efficiently using shared memory and overlapping computation with data transfer. Finally, the implementation is benchmarked against an existing C++ equivalent model.


References | Cited By  «-- Click to see who has cited this paper

[1] M.A. Bhuiyan, V. K. Pallipuram, M.C. Smith, "Acceleration of spiking neural networks in emerging multi-core and GPU architectures", IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010
[CrossRef] [SCOPUS Times Cited 51]


[2] R Mirsu, C. Caleanu, V. Tiponut, "GPU accelerated model for liquid state machine based on spiking neurons", 17th International Conference on Soft Computing (MENDEL), Brno, Czech Rep, 2011.

[3] J. M. Nageswaran, N. Dutt, J. L. Krichmar, A. Nicolau, A.V. Veidenbaum, "A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors," Special issue of Neural Network, Elsevier, vol.22, no. 5-6, pp. 791-800, July 2009 [PubMed]

[4] M. A. Bhuiyan, T. M. Taha, R. Jalasutram, "Character recognition with two spiking neural network models on multi-core architectures," in IEEE Proc. CIMSVP, TN, pp. 29 - 34, Mar. 2009.

[5] M. Herlihy, N. Shavit, The Art of Multiprocessor Programming Morgan Kaufmann, Burlington, MA, 2010.

[6] NVIDIA CUDATM. NVIDIA CUDA C Programming Guide 3.1.1. 2010.

[7] D. Kirk, W. Hwu, Programming Massively Parallel Processors Morgan Kaufmann, Burlington, MA, 2010.

[8] R. Ananthanarayanan, D. Modha, "Anatomy of a cortical simulator", Proc. Int'l Conference for High Performance Computing, Networking, Storage and Analysis, Nov. 2007, pp. 1-12.

[9] W. Gerstner, W. Kistler, Spiking Neuron Models, Cambridge University Press, 2002.

[10] E.M. Izhikevich, "Simple model of spiking neurons", IEEE Transactions on Neural Networks, Vol.14, No.6, 2003, pp. 1569-1572
[CrossRef] [PubMed] [Web of Science Times Cited 2869] [SCOPUS Times Cited 3284]


[11] E.M. Izhikevich, "Resonate and fire neurons", Neural Networks 14, pp. 883-894, 2001
[CrossRef] [Web of Science Times Cited 343] [SCOPUS Times Cited 375]


[12] E.M. Izhikevich, "Which model to use for cortical neurons?", IEEE Transactions on Neural Networks (Special Issue on Temporal Coding), 2004
[CrossRef] [Web of Science Times Cited 1632] [SCOPUS Times Cited 1845]


[13] W. Maass, "Networks of spiking neurons: The third generation of neural network models", Neural Networks, Vol. 10, No.9, pp. 1659-1671, 1997
[CrossRef] [Web of Science Times Cited 1715] [SCOPUS Times Cited 2010]


[14] L. F. Abbott, T. B. Kepler, "Model neurons: from Hodgkin-Huxley to Hopfield", Editor: Luis Garrido, Lecture Notes in Physics, vol. 368, p.5-18, 1990

[15] I. Bogdanov, R. Mirsu, V. Tiponut, "MATLAB model for spiking neural networks", Proc. of the 13th WSEAS International Conference on Systems, Rhodes, Greece, 23-25 July, 2009, pp. 533-537.

[16] R. Mirsu, V. Tiponut, I. Gavrilut, "Storing information with spiking neural networks", Proc. of the 13th WSEAS International Conference on Computers, Rhodes, Greece, 23-25 July 2009, pp. 318-322.

[17] S. Wills, Computation with Spiking Neurons, PhD Dissertation, University of Cambridge, 2004.

[18] R. Mirsu, V. Tiponut, "Parallel model for spiking neural networks using MATLAB", 9th International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania, 2010, pp. 369-372
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 4]


[19] W. Maass, T. Natschlager, H. Markram, "Real-Time Computing without stable states: A new Framework for Neural Computation based on Perturbation", Neural Computation, Vol. 14, No. 11, Pages 2531-2560, Nov. 2002
[CrossRef] [PubMed] [Web of Science Times Cited 2309] [SCOPUS Times Cited 2589]


[20] J. Kamarainen, V. Kyrki, H. Kalviainen, M. Hamouz, J. Kittler," Invariant Gabor features for face evidence extraction", Proceedings of the IAPR Workshop on Machine Vision Applications, Nara, Japan, 2002, pp. 228-231.

[21] O. Ayinde, Y .Yang, "Face recognition approach based on rank correlation of Gabor-filtered images", Pattern Recognition, vol. 35(6), pp. 1275-1289, 2002
[CrossRef] [Web of Science Times Cited 70] [SCOPUS Times Cited 87]


[22] V. Kyrki, J. Kamarainen, H. Kalviainen, "Content based image matching using Gabor filtering", Proceedings of the Int. Conf. on Advanced Concepts for Intelligent Vision Systems Theory and Applications, Baden-Baden, Germany, 2001, pp. 45-49.

[23] D. Martinez, E. Hugues, "A spiking neural network model of the locust antennal lobe: Towards neuromorphic electronic noses inspired from insect olfaction", Proceedings of the NATO Advance Research Workshop on Electronic Noses & Sensors for Detection of Explosives, Warwick, Coventry, U.K., 2003, pp. 209-234.



References Weight

Web of Science® Citations for all references: 8,939 TCR
SCOPUS® Citations for all references: 10,245 TCR

Web of Science® Average Citations per reference: 372 ACR
SCOPUS® Average Citations per reference: 427 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-05-17 20:42 in 57 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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