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.
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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
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DOI: https://doi.org/10.1007/11494669_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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