Optical Versus Electronic Implementation of Probabilistic Graphical Inference and Experimental Device Demonstration Using Nonlinear Photonics
The probabilistic inference model has been widely used in various areas, such as error-control coding, machine learning, speech recognition, artificial intelligence, and statistics. In this paper, we study both computation and communications power consumption of optical-based and electronic-based im...
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2018-01-01
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| author | Masoud Babaeian Patrick Keiffer Mark A. Neifeld Ratchaneekorn Thamvichai Robert A. Norwood Pierre-A. Blanche John Wissinger N. Peyghambarian |
| author_facet | Masoud Babaeian Patrick Keiffer Mark A. Neifeld Ratchaneekorn Thamvichai Robert A. Norwood Pierre-A. Blanche John Wissinger N. Peyghambarian |
| author_sort | Masoud Babaeian |
| collection | DOAJ |
| description | The probabilistic inference model has been widely used in various areas, such as error-control coding, machine learning, speech recognition, artificial intelligence, and statistics. In this paper, we study both computation and communications power consumption of optical-based and electronic-based implementations of the probabilistic inference algorithm used in solving large scale problems. Our analysis indicates that the optical implementation provides substantial reduction for power and area compare to the electronic-based solutions as problems become large. For a network with 1 million nodes and 100 alphabet size, our proposed wavelength multiplexed all-optical implementation requires approximately 200 kilowatts (kW) of power as compared with 1.47 gigawatts (GW) and 1.7 megawatts (MW) using CPU-based and subthreshold VLSI-based systems, respectively. The optical-based solution is tolerant to shot noise and imperfections of optical modules used in the architecture as well. We also performed an all-optical experimental verification of a graphical inference as the proof of concept and have demonstrated the essential mathematical operations, multiplication, and normalization (division), in photonics operations using nonlinear bulk materials. The normalization and multiplication are shown optically through a pump-probe saturation process and a logarithm-summation-exponential (log-sum-exp) operation, respectively. We used single mode silicon waveguide and single-wall carbon nanotube (SWCNT) as nonlinear optical materials to implement logarithm and exponential operations, respectively. The SWCNT is also used as the nonlinear component in the pump-probe saturation experiment to implement the normalization function. |
| format | Article |
| id | doaj-art-82dadc0204b6411ebef2c9dec300b258 |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-82dadc0204b6411ebef2c9dec300b2582025-08-20T03:30:56ZengIEEEIEEE Photonics Journal1943-06552018-01-0110511210.1109/JPHOT.2018.28718228471109Optical Versus Electronic Implementation of Probabilistic Graphical Inference and Experimental Device Demonstration Using Nonlinear PhotonicsMasoud Babaeian0https://orcid.org/0000-0002-3455-4744Patrick Keiffer1Mark A. Neifeld2Ratchaneekorn Thamvichai3Robert A. Norwood4Pierre-A. Blanche5https://orcid.org/0000-0002-9592-2010John Wissinger6N. Peyghambarian7College of Optical Sciences, University of Arizona, Tucson, AZ, USACollege of Optical Sciences, University of Arizona, Tucson, AZ, USACollege of Optical Sciences, University of Arizona, Tucson, AZ, USAElectrical and Computer Engineering, University of Arizona, Tucson, AZ, USACollege of Optical Sciences, University of Arizona, Tucson, AZ, USACollege of Optical Sciences, University of Arizona, Tucson, AZ, USACollege of Optical Sciences, University of Arizona, Tucson, AZ, USACollege of Optical Sciences, University of Arizona, Tucson, AZ, USAThe probabilistic inference model has been widely used in various areas, such as error-control coding, machine learning, speech recognition, artificial intelligence, and statistics. In this paper, we study both computation and communications power consumption of optical-based and electronic-based implementations of the probabilistic inference algorithm used in solving large scale problems. Our analysis indicates that the optical implementation provides substantial reduction for power and area compare to the electronic-based solutions as problems become large. For a network with 1 million nodes and 100 alphabet size, our proposed wavelength multiplexed all-optical implementation requires approximately 200 kilowatts (kW) of power as compared with 1.47 gigawatts (GW) and 1.7 megawatts (MW) using CPU-based and subthreshold VLSI-based systems, respectively. The optical-based solution is tolerant to shot noise and imperfections of optical modules used in the architecture as well. We also performed an all-optical experimental verification of a graphical inference as the proof of concept and have demonstrated the essential mathematical operations, multiplication, and normalization (division), in photonics operations using nonlinear bulk materials. The normalization and multiplication are shown optically through a pump-probe saturation process and a logarithm-summation-exponential (log-sum-exp) operation, respectively. We used single mode silicon waveguide and single-wall carbon nanotube (SWCNT) as nonlinear optical materials to implement logarithm and exponential operations, respectively. The SWCNT is also used as the nonlinear component in the pump-probe saturation experiment to implement the normalization function.https://ieeexplore.ieee.org/document/8471109/Nonlinear opticsnonlinear optical devicesoptical computingphotonicsultrafast optics. |
| spellingShingle | Masoud Babaeian Patrick Keiffer Mark A. Neifeld Ratchaneekorn Thamvichai Robert A. Norwood Pierre-A. Blanche John Wissinger N. Peyghambarian Optical Versus Electronic Implementation of Probabilistic Graphical Inference and Experimental Device Demonstration Using Nonlinear Photonics IEEE Photonics Journal Nonlinear optics nonlinear optical devices optical computing photonics ultrafast optics. |
| title | Optical Versus Electronic Implementation of Probabilistic Graphical Inference and Experimental Device Demonstration Using Nonlinear Photonics |
| title_full | Optical Versus Electronic Implementation of Probabilistic Graphical Inference and Experimental Device Demonstration Using Nonlinear Photonics |
| title_fullStr | Optical Versus Electronic Implementation of Probabilistic Graphical Inference and Experimental Device Demonstration Using Nonlinear Photonics |
| title_full_unstemmed | Optical Versus Electronic Implementation of Probabilistic Graphical Inference and Experimental Device Demonstration Using Nonlinear Photonics |
| title_short | Optical Versus Electronic Implementation of Probabilistic Graphical Inference and Experimental Device Demonstration Using Nonlinear Photonics |
| title_sort | optical versus electronic implementation of probabilistic graphical inference and experimental device demonstration using nonlinear photonics |
| topic | Nonlinear optics nonlinear optical devices optical computing photonics ultrafast optics. |
| url | https://ieeexplore.ieee.org/document/8471109/ |
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