Compact holographic optical neural network system for real-time pattern recognition

Thomas Taiwei Lu; David T. Mintzer; Andrew A. Kostrzewski; Freddie Shing-Hong Lin

doi:10.1117/1.600792

1 August 1996 Compact holographic optical neural network system for real-time pattern recognition

Thomas Taiwei Lu, David T. Mintzer, Andrew A. Kostrzewski, Freddie Shing-Hong Lin

Optical Engineering, Vol. 35, Issue 8, (August 1996). https://doi.org/10.1117/1.600792

One of the important characteristics 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 in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with onedimensional electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high-resolution volume holographic materials and is capable of performing massive 3-D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation-shift-scaleinvariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

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Thomas Taiwei Lu, David T. Mintzer, Andrew A. Kostrzewski, and Freddie Shing-Hong Lin "Compact holographic optical neural network system for real-time pattern recognition," Optical Engineering 35(8), (1 August 1996). https://doi.org/10.1117/1.600792

Published: 1 August 1996

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