Nonlinear optical components for all-optical probabilistic graphical model
Norwood, Robert A.
Allen, Taylor G.
Chen, Vincent W.
Perry, Joseph W.
Marder, Seth R.
Neifeld, Mark A.
AffiliationUniv Arizona, Dept Phys
Univ Arizona, Coll Opt Sci
Univ Arizona, Elect & Comp Engn
MetadataShow full item record
PublisherNATURE PUBLISHING GROUP
CitationBabaeian, M., Blanche, P. A., Norwood, R. A., Kaplas, T., Keiffer, P., Svirko, Y., ... & Marder, S. R. (2018). Nonlinear optical components for all-optical probabilistic graphical model. Nature communications, 9(1), 2128. https://doi.org/10.1038/s41467-018-04578-x
Rights© The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License.
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AbstractThe probabilistic graphical models (PGMs) are tools that are used to compute probability distributions over large and complex interacting variables. They have applications in social networks, speech recognition, artificial intelligence, machine learning, and many more areas. Here, we present an all-optical implementation of a PGM through the sum-product message passing algorithm (SPMPA) governed by a wavelength multiplexing architecture. As a proof-of-concept, we demonstrate the use of optics to solve a two node graphical model governed by SPMPA and successfully map the message passing algorithm onto photonics operations. The essential mathematical functions required for this algorithm, including multiplication and division, are implemented using nonlinear optics in thin film materials. The multiplication and division are demonstrated through a logarithm-summation-exponentiation operation and a pump-probe saturation process, respectively. The fundamental bottlenecks for the scalability of the presented scheme are discussed as well.
VersionFinal published version
SponsorsOffice of Naval Research (ONR) MURI program on Optical Computing [N00014-14-1-0505]; NSF ERC CIAN [EEC-0812072]; State of Arizona TRIF funding; Academy of Finland