Optical implementation of probabilistic graphical models

Pierre Alexandre Blanche, Masoud Babaeian, Madeleine Glick, John Wissinger, Robert Norwood, Nasser Peyghambarian, Mark Neifeld, Ratchaneekorn Thamvichai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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 10mm3, 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 an thermo-optics interferometer, and a wavelength remapping accomplished with a pump-probe amplifier.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Rebooting Computing, ICRC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509013708
DOIs
StatePublished - Nov 8 2016
Event2016 IEEE International Conference on Rebooting Computing, ICRC 2016 - San Diego, United States
Duration: Oct 17 2016Oct 19 2016

Publication series

Name2016 IEEE International Conference on Rebooting Computing, ICRC 2016 - Conference Proceedings

Other

Other2016 IEEE International Conference on Rebooting Computing, ICRC 2016
Country/TerritoryUnited States
CitySan Diego
Period10/17/1610/19/16

ASJC Scopus subject areas

  • Hardware and Architecture

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