Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154197
Title: Energy-efficient neural network inference with microcavity exciton polaritons
Authors: Matuszewski. M.
Opala, A.
Mirek, R.
Furman, M.
Król, M.
Tyszka, K.
Liew, Timothy Chi Hin
Ballarini, D.
Sanvitto, D.
Szczytko, J.
Piętka, B.
Keywords: Science::Physics
Issue Date: 2021
Source: Matuszewski. M., Opala, A., Mirek, R., Furman, M., Król, M., Tyszka, K., Liew, T. C. H., Ballarini, D., Sanvitto, D., Szczytko, J. & Piętka, B. (2021). Energy-efficient neural network inference with microcavity exciton polaritons. Physical Review Applied, 16(2), 024045-. https://dx.doi.org/10.1103/PhysRevApplied.16.024045
Project: MOE2019-T2-1-004
Journal: Physical Review Applied 
Abstract: We propose all-optical neural networks characterized by very high energy efficiency and performance density of inference. We argue that the use of microcavity exciton-polaritons allows to take advantage of the properties of both photons and electrons in a seamless manner. This results in strong optical nonlinearity without the use of optoelectronic conversion. We propose a design of a realistic neural network and estimate energy cost to be at the level of attojoules per bit, also when including the optoelectronic conversion at the input and output of the network, several orders of magnitude below state-of-the-art hardware implementations. We propose two kinds of nonlinear binarized nodes based either on optical phase shifts and interferometry or on polariton spin rotations.
URI: https://hdl.handle.net/10356/154197
ISSN: 2331-7019
DOI: 10.1103/PhysRevApplied.16.024045
Rights: © 2021 American Physical Society. All rights reserved. This paper was published in Physical Review Applied and is made available with permission of American Physical Society.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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