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Springer Verlag, Lecture Notes in Computer Science, p. 1253-1258

DOI: 10.1007/bfb0020323

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Handwritten Digit Recognition with Binary Optical Perceptron

Journal article published in 1997 by Indu Saxena, W. Gerstner, Perry Moerland ORCID, Emile Fiesler, A. Germond, M. Hasler, A. R. Pourzand, J. D. Nicoud
This paper is available in a repository.
This paper is available in a repository.

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Abstract

Binary weights are favored in electronic and optical hardware implementations of neural networks as they lead to improved system speeds. Optical neural networks based on fast ferroelectric liquid crystal binary level devices can benefit from the many orders of magnitudes improved liquid crystal response times. An optimized learning algorithm for all-positive perceptrons is simulated on a limited data set of hand-written digits and the resultant network implemented optically. First, gray-scale and then binary inputs and weights are used in recall mode. On comparing the results for the example data set, the binarized inputs and weights network shows almost no loss in performance.