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An efficient optical architecture for sparsely connected neural networksAn architecture for general-purpose optical neural network processor is presented in which the interconnections and weights are formed by directing coherent beams holographically, thereby making use of the space-bandwidth products of the recording medium for sparsely interconnected networks more efficiently that the commonly used vector-matrix multiplier, since all of the hologram area is in use. An investigation is made of the use of computer-generated holograms recorded on such updatable media as thermoplastic materials, in order to define the interconnections and weights of a neural network processor; attention is given to limits on interconnection densities, diffraction efficiencies, and weighing accuracies possible with such an updatable thin film holographic device.
Document ID
19910048095
Document Type
Conference Paper
Authors
Hine, Butler P., III (NASA Ames Research Center Moffett Field, CA, United States)
Downie, John D. (NASA Ames Research Center Moffett Field, CA, United States)
Reid, Max B. (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1990
Subject Category
OPTICS
Meeting Information
Advances in Optical Information Processing IV(Orlando, FL)
Distribution Limits
Public
Copyright
Other