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Proceedings Paper

Performance evaluation of a holographic optical neural network system
Author(s): Thomas Taiwei Lu; Andrew A. Kostrzewski; Hung Chou; Shudong Wu; Freddie Shing-Hong Lin
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Paper Abstract

One of the most outstanding properties of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced to the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections number with one-dimensional (1-D) electronic wires. High resolution pattern recognition problems may require a large number of neurons for parallel processing of the image. The holographic optical neural network (HONN) based on high resolution volume holographic materials is capable of providing 3-D massive parallel interconnection of tens of thousand of neurons. A HONN with 3600 neurons, contained in a portable briefcase, has been developed. Rotation-shift-scale invariant pattern recognition operations have been demonstrated with this system. System parameters, such as signal-to-noise ratio, dynamic range, and processing speed, will be discussed.

Paper Details

Date Published: 2 February 1993
Proc. SPIE 1773, Photonics for Computers, Neural Networks, and Memories, (2 February 1993); doi: 10.1117/12.983187
Show Author Affiliations
Thomas Taiwei Lu, Photonics Research (United States)
Andrew A. Kostrzewski, Physical Optics Corp. (United States)
Hung Chou, Physical Optics Corp. (United States)
Shudong Wu, Physical Optics Corp. (United States)
Freddie Shing-Hong Lin, Physical Optics Corp. (United States)

Published in SPIE Proceedings Vol. 1773:
Photonics for Computers, Neural Networks, and Memories
Stephen T. Kowel; John A. Neff; William J. Miceli, Editor(s)

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