Abstract
Recent trends in microprocessor design clearly show that the multicore processors are the answer to the question how to scale up the processing power of today’s computers. In this article we present our C implementation of the Temporal Hebbian Self-organizing Map (THSOM) neural network. This kind of neural networks have growing computational complexity for larger networks, therefore we present different approaches to the parallel processing – instruction based parallelism and data-based parallelism or their combination. Our C implementation of THSOM is modular and multi-platform, allowing us to move critical parts of the algorithm to other cores, platforms or use different levels of the instruction parallelism yet still run exactly the same computational flows – maintaining good comparability between different setups. For our experiments, we have chosen a multicore x86 system.
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
Kohonen, T.: Self-Organizing Maps3, 3rd edn. Springer, Heidelberg (2001)
Koutnik, J.: Inductive Modelling of Temporal Sequences by Means of Self-organization. In: Proceeding of Internation Workshop on Inductive Modelling (IWIM 2007), CTU in Prague, pp. 269–277 (2007) ISBN 978-80-01-03881-9
Koutnik, J.: Self-organizing Maps for Modeling and Recognition of Temporal Sequences, A thesis submitted to Faculty of Electrical Engineering, Czech Technical University in Prague (2008)
Porrmann, M., Witkowski, U., Rueckert, U.: A massively parallel architecture for self-organizing feature maps. IEEE Transactions on Neural Networks 14(5), 1110-1121 (2003)
Hendry, D., Duncan, A., Lightowler, A.: Ip core implementation of a self-organizing neural network. IEEE Transactions on Neural Networks 14(5), 1085–1096 (2003)
Franzmeier, M., Pohl, C., Porrmann, M., Ruekert, U.: Hardware Accelerated Data Analysis Parallel Computing in Electrical Engineering. In: International Conference on PARELEC 2004, September 7-10, 2004, pp. 309–314 (2004)
Rauber, A., Tomsich, P., Merkl, D.: A Parallel Implementation of the Self-Organizing Map Exploiting Cache Effects: Making the SOM Fit for Interactive High-Performance Data Analysis Neural Networks. In: IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marek, R., Skrbek, M. (2008). Efficient Implementation of the THSOM Neural Network. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-87559-8_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87558-1
Online ISBN: 978-3-540-87559-8
eBook Packages: Computer ScienceComputer Science (R0)