We describe a laser diode-based optoelectronic implementation of artificial neural networks which utilizes real-time holography in photorefractive crystals. The use of a laser diode light source reduces the system size and power requirements. The holographic material is rhodium- doped BaTiO₃ which has enhanced sensitivity at the laser-diode wavelength of 830 nm. A balanced coherent-detection method is used to represent bipolar optical neurons and weights. In addition, by distributing each neuron weight among a set of spatially and angularly distributed gratings using beam fanning, Bragg degeneracy and its associated inter-neuron optical crosstalk is virtually eliminated. The structure of the neural network is programmable and we have implemented a variety of neural networks including backpropagation and Kohonen-style self-organizing maps with up to 10,000 neurons and performance of up to 10⁸ weights processed per second during learning and readout. We also discuss weight decay in photorefractive materials, specifically its relative effects in the neural network and data storage domains.

Date Published: 28 August 1995
PDF: 8 pages
Proc. SPIE 2565, Optical Implementation of Information Processing, (28 August 1995); doi: 10.1117/12.217654

Published in SPIE Proceedings Vol. 2565:
Optical Implementation of Information Processing
Bahram Javidi; Joseph L. Horner, Editor(s)