Abstract
This presentation has two goals: (i) to review the recently suggested concept of bio-inspired CrossNet architectures for future hybrid CMOL VLSI circuits and (ii) to present new results concerning the prospects and problems of using these neuromorphic networks as classifiers of very large patterns, in particular of high-resolution optical images. We show that the unparalleled density and speed of CMOL circuits may enable to perform such important and challenging tasks as, for example, online recognition of a face in a high-resolution image of a large crowd.
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
International Technology Roadmap for Semiconductors, 2003 Edition, 2004 Update, available online at http://public.itrs.net/
Likharev, K.K.: Electronics Below 10 nm. In: Greer, J., et al. (eds.) Nano and Giga Challenges in Microelectronics, pp. 27–68. Elsevier, Amsterdam (2003)
Likharev, K.K., Strukov, D.B.: CMOL: Devices, Circuits, and Architectures. In: Cuniberti, G., et al. (eds.) Introduction to Molecular Electronics. Springer, Berlin (2005)
Strukov, D.B., Likharev, K.K.: A Reconfigurable Architecture for Hybrid CMOS/Nanoscale Circuits (with D. B. Strukov). Report at FCCM 2005, to be published (2005)
Strukov, D.B., Likharev, K.K.: Prospects for Terabit-scale Nanoelectronic Memories. Nanotechnology 16, 137–148 (2005)
Fölling, S., Türel, Ö., Likharev, K.K.: Single-Electron Latching Switches as Nanoscale Synapses. In: Proceedings of the IJCNN, pp. 216–221 (2001)
Türel, Ö., Likharev, K.K.: CrossNets: Possible Neuromorphic Networks Based on Nanoscale Components. Int. J. of Circ. Theor. Appl. 31, 37-53 (2003)
Türel, Ö., Lee, J.H., Ma, X., Likharev, K.K.: Neuromorphic Architectures for Nanoelectronic Circuits. Int. J. of Circ. Theor. Appl. 32, 277–302 (2004)
See, e.g., Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Perseus, Cambridge, MA (1991)
LeCun, Y., et al.: Gradient-based Learning Applied to Document Recognition. Proc. IEEE 86, 2278–2324 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, J.H., Likharev, K.K. (2005). CMOL CrossNets as Pattern Classifiers. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_55
Download citation
DOI: https://doi.org/10.1007/11494669_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
eBook Packages: Computer ScienceComputer Science (R0)