Optical Implementation of Probabilistic Graphical Models
Abstract
We are investigating the use of optics to solve highly connected graphical models by probabilistic inference, and more specifically the sum-product message passing algorithm. We are examining the fundamental limit of size and... [ view full abstract ]
We are investigating the use of optics to solve highly connected graphical models by probabilistic inference, and more specifically the sum-product message passing algorithm. We are examining the fundamental limit of size and power requirement according to the best multiplexing strategy we have found. For a million nodes, and an alphabet of a hundred, we found that the minimum size for the optical implementation is 10mm 3 , and the lowest bound for the power is 200 watts for operation at the shot noise limit. The various functions required for the algorithm to be operational are presented and potential implementations are discussed. These include a vector matrix multiplication using spectral hole burning, a logarithm carried out with two photon absorption, an exponential performed with saturable absorption, a normalization executed with a thermo-optics interferometer, and a wavelength remapping accomplished with a pump-probe amplifier.
Authors
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Pierre Blanche
(The University of Arizona)
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Masoud Babaeian
(The University of Arizona)
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Ratchaneekorn Thamvichai
(The University of Arizona)
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Madeleine Glick
(The University of Arizona)
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John Wissinger
(The University of Arizona)
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Robert Norwood
(The University of Arizona)
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Nasser Peyghambarian
(The University of Arizona)
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Mark Neifeld
(The University of Arizona)
Topic Area
Topics: Optical computing
Session
OS-04A » Optical and Quantum Computing 1 (10:15 - Tuesday, 18th October, Del Mar Ballroom C)
Paper
ID099_ICRC2016.pdf
Presentation Files
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