Abstract
If the dimensionality of an application problem is not very high (say, a few dozen inputs and outputs at most) and computing need not be performed in real time, most ANN algorithms can be implemented by pure software, especially if contemporary workstation computers are available. However, especially in real-time pattern recognition or robotics applications one might need special co-processor boards or even “neurocomputers.” For really large problems, e.g. in the preprocessing stages for more complicated computer vision, special hardware networks may have to be developed. Such ANN circuits are sometimes necessary to miniaturize devices and make them cheap, for instance in medical applications or consumer electronics.
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
K. Steinbuch: Automat und Mensch (Springer, Berlin, Heidelberg 1963)
B. Gilbert: IEEE J. Solid-State Circuits, SC-3 p.365 (1968)
T. Kohonen: In Proc. WCNN’93, World Congress on Neural Networks (Lawrence Erlbaum, Hillsdale, NJ 1993) p. IV–1
V. Pulkki. M.Sc. Thesis (Helsinki University of Technology, Espoo, Finland 1994)
H. P. Siemon, A. Ultsch: In Proc. INNC-90 Int. Neural Network Conf. (Kluwer, Dordrecht, Netherlands 1990) p. 643
R. Togneri, Y. Attikiouzel: In Proc. IJCNN-91-Singapore, Int. Joint Conf. on Neural Networks (IEEE Comput. Soc. Press, Los Alamitos, CA 1991) p. II–1717
F. Blayo, C. Lehmann: In Proc. INNC 90, Int. Neural Network Conf. (Kluwer, Dordrecht, Netherlands 1990)
R. Mann, S. Haykin: In Proc. IJCNN-90-WASH-DC, Int. Joint Conf. on Neural Networks (Lawrence Erlbaum, Hillsdale, NJ 1990) p. II–84
C. Lehmann, F. Blayo: In Proc. VLSI for Artificial Intelligence and Neural Networks, ed. by J. G. Delgado-Frias, W. R. Moore (Plenum, New York, NY 1991) p. 325
H. Speckmann, P. Thole, W. Rosentiel: In Artificial Neural Networks, 2, ed. by I. Aleksander, J. Taylor (North-Holland, Amsterdam, Netherlands 1992) p. II–1451
H. Speckmann, P. Thole, W. Rosenstiel: In Proc. ICANN-93, Int. Conf. on Artificial Neural Networks, ed. by S. Gielen, B. Kappen (Springer, London, UK 1993) p. 1040
H. Speckmann, P. Thole, W. Rosenthal: In Proc. IJCNN-93-Nagoya, Int. Joint Conf. on Neural Networks (IEEE Service Center, Piscataway, NJ 1993) p. 11–1951
M. S. Melton, T. Phan, D. S. Reeves, D. E. Van den Bout: IEEE Trans, on Neural Networks 3, 375 (1992)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kohonen, T. (1995). Hardware for SOM. In: Self-Organizing Maps. Springer Series in Information Sciences, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-97610-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-97610-0_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-97612-4
Online ISBN: 978-3-642-97610-0
eBook Packages: Springer Book Archive