All-optical graphical models for probabilistic inference

Pierre Alexandre Blanche; Madeleine Glick; John Wissinger; Khanh Kieu; Masoud Babaeian; Houman Rastegarfar; Veysi Demir; Mehmetcan Akbulut; Patrick Keiffer; Robert A. Norwood; Nasser Peyghambarian; Mark Neifeld

doi:10.1109/PHOSST.2016.7548802

All-optical graphical models for probabilistic inference

Pierre Alexandre Blanche, Madeleine Glick, John Wissinger, Khanh Kieu, Masoud Babaeian, Houman Rastegarfar, Veysi Demir, Mehmetcan Akbulut, Patrick Keiffer, Robert A. Norwood, Nasser Peyghambarian, Mark Neifeld

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

Considering that high performance electronic computation has become extremely efficient, for an optical hardware accelerator to be relevant, it must solve a type or a set of problems where its electronic counterpart is still struggling in term of size, energy, or time. We have identified one such challenge as the minimization of large scale Ising Hamiltonians when the number of particles is on the order of a million. Here we discuss an algorithmic approach based on probabilistic inference using graphical model and message passing.

Original language	English (US)
Title of host publication	2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	199-200
Number of pages	2
ISBN (Electronic)	9781509019007
DOIs	https://doi.org/10.1109/PHOSST.2016.7548802
State	Published - Aug 22 2016
Event	2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016 - Newport Beach, United States Duration: Jul 11 2016 → Jul 13 2016

Publication series

Name	2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016

Other

Other	2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016
Country/Territory	United States
City	Newport Beach
Period	7/11/16 → 7/13/16

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing
Atomic and Molecular Physics, and Optics
Electrical and Electronic Engineering

Access to Document

10.1109/PHOSST.2016.7548802

Cite this

Blanche, P. A., Glick, M., Wissinger, J., Kieu, K., Babaeian, M., Rastegarfar, H., Demir, V., Akbulut, M., Keiffer, P., Norwood, R. A., Peyghambarian, N., & Neifeld, M. (2016). All-optical graphical models for probabilistic inference. In 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016 (pp. 199-200). Article 7548802 (2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PHOSST.2016.7548802

All-optical graphical models for probabilistic inference. / Blanche, Pierre Alexandre; Glick, Madeleine; Wissinger, John et al.
2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 199-200 7548802 (2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Blanche, PA, Glick, M, Wissinger, J, Kieu, K, Babaeian, M, Rastegarfar, H, Demir, V, Akbulut, M, Keiffer, P, Norwood, RA , Peyghambarian, N & Neifeld, M 2016, All-optical graphical models for probabilistic inference. in 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016., 7548802, 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 199-200, 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016, Newport Beach, United States, 7/11/16. https://doi.org/10.1109/PHOSST.2016.7548802

Blanche PA, Glick M, Wissinger J, Kieu K, Babaeian M, Rastegarfar H et al. All-optical graphical models for probabilistic inference. In 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 199-200. 7548802. (2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016). doi: 10.1109/PHOSST.2016.7548802

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abstract = "Considering that high performance electronic computation has become extremely efficient, for an optical hardware accelerator to be relevant, it must solve a type or a set of problems where its electronic counterpart is still struggling in term of size, energy, or time. We have identified one such challenge as the minimization of large scale Ising Hamiltonians when the number of particles is on the order of a million. Here we discuss an algorithmic approach based on probabilistic inference using graphical model and message passing.",

author = "Blanche, {Pierre Alexandre} and Madeleine Glick and John Wissinger and Khanh Kieu and Masoud Babaeian and Houman Rastegarfar and Veysi Demir and Mehmetcan Akbulut and Patrick Keiffer and Norwood, {Robert A.} and Nasser Peyghambarian and Mark Neifeld",

note = "Funding Information: Authors acknowledge the support of the Office of Naval Research under grant #N00014-14-1-0505, as well the NSF ERC CIAN under grant #EEC-0812072. Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016 ; Conference date: 11-07-2016 Through 13-07-2016",

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AU - Demir, Veysi

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All-optical graphical models for probabilistic inference

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