Energy- and size-efficient ultra-fast plasmonic circuits for neuromorphic computing architectures
Energy- and size-efficient ultra-fast plasmonic circuits for neuromorphic computing architectures
Energy- and size-efficient ultra-fast plasmonic circuits for neuromorphic computing architectures

Journal Publications

  • G. Mourgias-Alexandris, G. Dabos, N. Passalis, A. R. Totovic, A. Tefas and N. Pleros, "All-optical WDM Recurrent Neural Networks with Gating," in IEEE Journal of Selected Topics in Quantum Electronics, doi: 10.1109/JSTQE.2020.2995830.
  • A. R. Totović, G. Dabos, N. Passalis, A. Tefas and N. Pleros, "Femtojoule per MAC Neuromorphic Photonics: An Energy and Technology Roadmap," in IEEE Journal of Selected Topics in Quantum Electronics, vol. 26, no. 5, pp. 1-15, Sept.-Oct. 2020, Art no. 8800115, doi: 10.1109/JSTQE.2020.2975579.
  • G. Mourgias-Alexandris et al., "Neuromorphic Photonics With Coherent Linear Neurons Using Dual-IQ Modulation Cells," in Journal of Lightwave Technology, vol. 38, no. 4, pp. 811-819, 15 Feb.15, 2020, doi: 10.1109/JLT.2019.2949133.
  • G. Mourgias-Alexandris, N. Passalis, G. Dabos, A. Totović, A. Tefas and N. Pleros, "A Photonic Recurrent Neuron for Time-Series Classification," in Journal of Lightwave Technology, vol. 39, no. 5, pp. 1340-1347, 1 March1, 2021, doi: 10.1109/JLT.2020.3038890.
  • Vangelidis Ioannis, Bellas Dimitris, Suckow Stephan, Dabos George, Koppens Frank, Ferrari Andrea, Pleros Nikos and Lidorikis Elefterios, “Unbiased plasmonic-assisted integrated graphene photodetectors” submitted at ACS Nano.
  • T. Rutirawut, W. Talataisong and F. Y. Gardes, "Designs of Silicon Nitride Slot Waveguide Modulators With Electro-Optic Polymer and the Effect of Induced Charges in Si-Substrate on Their Performance," in IEEE Photonics Journal, vol. 13, no.2, pp. 1-15, doi: 10.1109/JPHOT.2021.3059276.
  • A. Messner et al., “Broadband Metallic Fiber-to-Chip Couplers and a Low-Complexity Integrated Plasmonic Platform”, Nano Letters, 2021, 10.3929/ethz-b-000493455.
  • J. Faneca et. al, “Towards low loss non-volatile phase change materials in mid index waveguides”, 10.1088/2634-4386/ac156e, http://arxiv.org/abs/2101.11127.
  • R. Stabile, G. Dabos, C. Vagionas, B. Shi, N. Calabretta, N. Pleros, “Neuromorphic Photonics: 2D or not 2D?”, J. of Applied Physics, Vol 129, Νο. 20, 10.1063/5.0047946
  • M. Moralis-Pegios et al, “Perfect Linear Optics using Silicon Photonics”, arxiv:2306.17728
  • S. Kovaios, A. Tsakyridis, G. Giamougiannis, K. Fotiadis, D. Sacchetto, M. Zervas, M. Moralis-Pegios, and N. Pleros, “Generalized Mach Zehnder interferometers integrated on Si3N4 waveguide platform,” IEEE J. on Sel. Topics of Quantum Electron, vol. 29, no. 6: Photonic Signal Processing, pp. 1–9, 2023.
  • C. Pappas, S. Kovaios, M. Moralis-Pegios, A. Tsakyridis, G. Giamougiannis, M. Kirtas, J. Van Kerrebrouck, G. Coudyzer, X. Yin, N. Passalis, A. Tefas, N. Pleros, "Programmable Tanh-, ELU-, Sigmoid-, and Sin-Based Nonlinear Activation Functions for Neuromorphic Photonics," in IEEE Journal of Selected Topics in Quantum Electronics, vol. 29, no. 6: Photonic Signal Processing, pp. 1-10, Nov.-Dec. 2023, Art no. 6101210, doi: 10.1109/JSTQE.2023.3277118.
  • M. Kirtas, N. Passalis, A. Oikonomou, M. Moralis-Pegios, G. Giamougiannis, A. Tsakyridis, G. Mourgias-Alexandris, N. Pleros, A. Tefas, “Mixed-precision quantization-aware training for photonic neural networks.”, Neural Comput & Applic (2023). https://doi.org/10.1007/s00521-023-08848-8
  • A. Tsakyridis et al, “Photonic Neural Network Fundamentals: Optics-informed Deep Learning over Neuromorphic Photonic Hardware”, submitted at APL Photonics (Invited Tutorial) 2023
  • S. Kovaios et. al., “Programmable Tanh- and ELU-based Photonic Neurons in Optics-Informed Neural Networks”, submitted at JLT 2023
  • G. Giamougiannis, A. Tsakyridis, Miltiadis Moralis-Pegios, Christos Pappas, Manos Kirtas, Nikolaos Passalis, David Lazovsky, Anastasios Tefas, N. Pleros, “Analog nanophotonic computing going practical: Silicon Photonic Deep Learning engines for tiled optical matrix multiplication with dynamic precision,” Nanophotonics, vol. 12, no. 5, 2023, pp. 963-973. Nanophotonics 2023 DOI:10.1515/nanoph-2022-0423
  • G. Giamougiannis, A. Tsakyridis, Miltiadis Moralis-Pegios, George Mourgias-Alexandris, Angelina R. Totovic, George Dabos, Manos Kirtas, Nikolaos Passalis, Anastasios Tefas, Dimitrios Kalavrouziotis, Dimitris Syrivellis, Paraskevas Bakopoulos, Elad Mentovich, David Lazovsky, N. Pleros, “Neuromorphic silicon photonics with 50 GHz Tiled Matrix Multiplication for DL applications”, Adv. Photon. 5(1) 016004 (1 February 2023) https://doi.org/10.1117/1.AP.5.1.016004
  • G. Dabos, D. V. Bellas, R. Stabile, M. Moralis-Pegios, G. Giamougiannis, A. Tsakyridis, A. Totovic, E. Lidorikis, and N. Pleros, "Neuromorphic photonic technologies and architectures: scaling opportunities and performance frontiers [Invited]," Opt. Mater. Express 12, 2343-2367 (2022).
  • M. Moralis-Pegios, G. Mourgias-Alexandris, A. Tsakyridis, G. Giamougiannis, A. Totovic, G. Dabos, N. Passalis, M. Kirtas, T. Rutirawut, F. Y. Gardes, A. Tefas and N. Pleros, "Neuromorphic Silicon Photonics and Hardware-Aware Deep Learning for High-Speed Inference," in Journal of Lightwave Technology, vol. 40, no. 10, pp. 3243-3254, 15 May15, 2022.
  • G. Giamougiannis, A. Tsakyridis, Miltiadis Moralis-Pegios, Angelina R. Totovic, Manos Kirtas, Nikolaos Passalis, Anastasios Tefas, David Lazovsky, N. Pleros, “Universal Linear Optics Revisited: New Perspectives for Neuromorphic Computing with Silicon Photonics" in IEEE Journal of Selected Topics in Quantum Electronics, vol. 29, no. 2: Optical Computing, pp. 1-16, March-April 2023, Art no. 6200116, doi: 10.1109/JSTQE.2022.3228318.

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