Abstract
The implementation for the digital neural networks on a chip requires a lot of chip area consumption. Our contribution paper deals therefore with the design of a novel type of digital CNN architecture focused on pattern recognition application. The novel designed network we compare with another two CNN implementation of digital network on a chip used for pattern recognition by the selected parameters as the speed and chip area consumption. From the comparison we can recognize that our proposed digital CNN network is the best from the other ones.
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
Chua, L.O., Yang, L.: Cellular Neural Network: Theory. IEEE Trans. Circuits Systems 35, 1257–1272 (1988)
Chua, L.O., Yang, L.: Cellular Neural Network: Aplications. IEEE Trans. Circuits Systems 35, 1273–1290 (1988)
Hänggi, M., Moschytz, G.S.: Cellular Neural Network: Analysis, Design and Optimalization. Kluwer Academic Publisher, Boston (2000)
Sordo, M.: Introduction to Neural Networks in Healthcare. Open Clinical knowledge management for medical care (October 2002)
Larsen, J.: Introduction to Artifical Neural Network, Section for Digital Signal Processing Department of Mathematical Modeling Technical University of Denmark, 1st edn (November 1999)
Seung, S.: Introduction to Neural Networks: Lecture 1, The Massachusetts Institute of Technology. The Seung Lab. (September 2002)
Kvasnička, V., Beňušková, Ľ., Pospíchal, J., Farkaš, I., Tiňo, P., Kráľ, A.: Úvod do teórie neurónových sietí. In: IRIS (1997) ISBN 80-88778-30-1
Jeon, J.: Fuzzy and Neural Network Models for Analyses of Piles, A dissertation submitted to the Graduate Faculty of North Carolina State University, Raleigh, North Carolina (August 2007)
Restrepo, H.F., Hoffman, R., Perez-Uribe, A., Teuscher, C., Sanchez, E.: A Networked FPGA-Based Hardware Implementation of a Neural Network Application. In: Pocek, K.L., Arnold, J.M. (eds.) Proceedings of the IEEE Symposium on Field Programmable Custom Computing Machines, FCCM 2000, Napa, California, USA, April 17-19, pp. 337–338. IEEE Computer Society, Los Alamitos (2000)
Muthuramalingam, A., Himavathi, S., Srinivasan, E.: Neural Network Implementation Using FPGA: Issues and Application. International Journal of Information Technology 4(2) (28-08-2008)
Boubaker, M., Khalifa, K.B., Girau, B., Dogui, M., Bedoui, M.H.: On-Line Arithmetic Based Reprogrammable Hardware Implementation of LVQ Neural Network for Alertness Classification. IJCSNS International Journal of Computer Science and Network Security 8(3) (March 2008)
Larkin, D., Kinane, A., Muresan, V., O’Connor, N.: An Efficient Hardware Architecture for a Neural Network Activation Function Generator. LNCS. Springer, Heidelberg (2006)
Raschman, E., Ďuračková, D.: The Novel Digital Design of a Cell for Cellular Neural Network. In: Proceedings of the Electronic Devices and Systems, Brno, September 2008, pp. 322–325 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Raschman, E., Ďuračková, D. (2009). Area Chip Consumption by a Novel Digital CNN Architecture for Pattern Recognition. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-04274-4_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04273-7
Online ISBN: 978-3-642-04274-4
eBook Packages: Computer ScienceComputer Science (R0)