Abstract
There has been an increasing use of the graphic processing unit (GPU) in many areas including artificial neural networks (ANN) for several years. However, reported works concentrate on the application itself and not on the methodology used to implement the ANN model in the GPU. This paper presents a set of practical aspect to be considered by new GPU user in the implementation of ANN in GPUs. To illustrate the proposed aspects, the paper describes the realization of the Pulse Coupled Neural Network (PCNN), an iterative model, following these aspects and discusses the problematic of synchronization presented in this and other ANN models that is not treated in other works.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Nickolls, J., Dally, W.J.: The GPU computing era. IEEE Micro 30(2), 56–69 (2010)
Rouhipoura, M., Bentley, P.J.: Fast bio-inspired computation using a GPU-based systemic computer. Parallel Computing 36, 591–617 (2010)
Zhongwen, L., Hongzhi, L., Xincai, W.: Artificial neural network computation on graphic process unit. In: Proceeding of International Joint Conference on Neural Networks, vol. 10, pp. 622–626 (2005)
Dolan, R., DeSouza, G.: GPU-based simulation of cellular neural networks for image processing. In: Proceeding of International Joint Conference on Neural Networks, pp. 730–735 (June 2009)
Ermai, X., McGinnity, M., QingXiang, Jianyong, W.C., Rontaig, C.: GPU implementation of spiking neural networks for color image segmentation. In: 2011 4th Inter. Congress Image and Signal Processing (CISP), vol. 3, pp. 1246–1250 (October 2011)
Peniak, M., Morse, A., Larcombe, C., Ramirez-Contla, S., Cangelos, A.: Aquila: An open-source GPU-accelerated toolkit for cognitive and neuro-robotics research. In: Proceeding of Inter. Joint Conference on Neural Networks, pp. 1753–1760 (August 2011)
Honghoon, J., Anjin, P., Keechul, J.: Neural network implementation using CUDA and OpenMP. In: Proceeding of Computing: Techniques and Applications, pp. 155–161 (2008)
Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E., Phillips, J.C.: GPU Computing. Proceedings of the IEEE 96(5), 879–899 (2008)
Eckhorn, B.R., Reitboeck, H.J., Arndt, M., Dicke, P.: Feature linking via stimulus-evoked oscillations experimental results from cat visual cortex and functions implications from a network model. In: Proceeding of International Joint Conference on Neural Networks, pp. 723–730 (October 1989)
Johnson, J.L., Padgett, M.: PCNN models and applications. IEEE Transactions on Neural Networks 10(3), 480–498 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Chacon-Murguia, M.I., Cardona-Soto, J.A. (2013). Practical Aspects on the Implementation of Iterative ANN Models on GPU Technology. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Recent Advances on Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33021-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-33021-6_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33020-9
Online ISBN: 978-3-642-33021-6
eBook Packages: EngineeringEngineering (R0)