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An optically programmed neural network
IEEE International Conference on Neural Networks, 1988The author report on the design, construction and operation of a hybrid electrooptic computer intended for neural-network applications. They have configured this system to implement what they believe is the largest fully interconnected neural network built to date.
Cary D. Kornfeld +3 more
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Optics and Photonics News, 1990
The trade-off between the number of neurons that can be implemented with a single correlator and the shift invariance that each neuron has is investigated. A new type of correlator implemented with a planar hologram is described whose shift invariance can be controlled by setting the position of the hologram properly.
Demitri Psaltis, Yong Quio
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The trade-off between the number of neurons that can be implemented with a single correlator and the shift invariance that each neuron has is investigated. A new type of correlator implemented with a planar hologram is described whose shift invariance can be controlled by setting the position of the hologram properly.
Demitri Psaltis, Yong Quio
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Optical neural network with bipolar neural states
Applied Optics, 1992A method to achieve bipolar performance in a single-channel optical associative memory is presented. By coding the biased interconnection weights, a distributed background, and an input-dependent dynamic threshold on a single mask, we construct an optical network with both bipolar neural states and bipolar interconnections.
X M, Wang, G G, Mu
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Optical processor for a binarized neural network
Optics Letters, 2022We propose and experimentally demonstrate an optical processor for a binarized neural network (NN). Implementation of a binarized NN involves multiply-accumulate operations, in which positive and negative weights should be implemented.
Long Huang, Jianping Yao
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Optical processing in neural networks
IEEE Expert, 1992Hybrid neural network hardware and several algorithms that use optical processing at various stages for image processing and pattern recognition are described. The implementations of the algorithms in associative processors, optimization neural networks, symbolic correlator neural networks, production system neural networks, and adaptive neural ...
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Optics and Photonics News, 2020
Light-based computers inspired by the human brain could transform machine learning—if they can be scaled up.
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Light-based computers inspired by the human brain could transform machine learning—if they can be scaled up.
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Optical Neural Network Architecture in Photoferroelectrics
Ferroelectrics, 2003We present a novel, versatile optoelectronic neural network architecture for implementing supervised learning in photo-ferroelectrics (Sr x Ba 1-x Nb 2 O 6 , Bi 12 XO 20 ; X=Ge, Si, Ti, LiNbO 3 :Fe, LiTaO 3 :Fe and LiTaO 3 :Cr). The system is based on spatial multiplexing rather than the more commonly used angular multiplexing of interconnect gratings.
Babur Y., Mamedov A.M.
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All-optical recurrent neural network
SPIE Proceedings, 1998We report on the optical setup, device characterization and performance in a pattern recognition task of a neural network with 256 neurons and optical feedback.
Berger, Ch., Collings, N.
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A continuous-time optical neural network
IEEE International Conference on Neural Networks, 1988A description is given of the architecture and functioning of an all-optical, continuous-time recurrent neural network. The network is a ring resonator which contains a saturable, two-beam amplifier, two volume holograms, and a linear, two-beam amplifier.
Harold M. Stoll, L.-S. Lee
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Neural-network approach for optical tomography
Signal Processing, 2006The problem of optical tomography reconstruction is an ill-posed problem and the errors in the measurement data will be amplified in the reconstructed results. In order to fix the problem of ill-posedness, some a priori information should be incorporated in the process of reconstruction.
Jiajun Wang +3 more
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