@inproceedings{5ffdb76ae18144b69cb5d0a65a601374,
title = "Deep Neural Networks with Time-Domain Synthetic Photonic Lattices",
abstract = "Optical neural networks (ONN) attracts a lot of attention based on their potential to offer orders-of-magnitude faster performance and lower energy consumption compared to purely electronic systems. There are strong advances in processing of spatially- and frequency-encoded optical data [1]. However the ONN development for coherent manipulation of optical pulse trains remains an open challenge, which could lead to advances in communications and real-time data analysis.",
author = "Pankov, {Artem V.} and Sidelnikov, {Oleg S.} and Vatnik, {Ilya D.} and Churkin, {Dmitry V.} and Sukhorukov, {Andrey A.}",
note = "Funding Information: This work is supported by Ministry of Education and Science of the Russian Federation (FSUS-2020-0034) and the Australian Research Council (DP190100277). Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021 ; Conference date: 21-06-2021 Through 25-06-2021",
year = "2021",
month = jun,
doi = "10.1109/CLEO/Europe-EQEC52157.2021.9542271",
language = "English",
series = "2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021",
address = "United States",
}