Search Results - "Kirtas, M."

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  1. 1

    Noise-resilient and high-speed deep learning with coherent silicon photonics by Mourgias-Alexandris, G., Moralis-Pegios, M., Tsakyridis, A., Simos, S., Dabos, G., Totovic, A., Passalis, N., Kirtas, M., Rutirawut, T., Gardes, F. Y., Tefas, A., Pleros, N.

    Published in Nature communications (23-09-2022)
    “…The explosive growth of deep learning applications has triggered a new era in computing hardware, targeting the efficient deployment of multiply-and-accumulate…”
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    Journal Article
  2. 2

    Channel response-aware photonic neural network accelerators for high-speed inference through bandwidth-limited optics by Mourgias-Alexandris, G, Moralis-Pegios, M, Tsakyridis, A, Passalis, N, Kirtas, M, Tefas, A, Rutirawut, T, Gardes, F Y, Pleros, N

    Published in Optics express (28-03-2022)
    “…Photonic neural network accelerators (PNNAs) have been lately brought into the spotlight as a new class of custom hardware that can leverage the maturity of…”
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    Journal Article
  3. 3

    Quantization-aware training for low precision photonic neural networks by Kirtas, M., Oikonomou, A., Passalis, N., Mourgias-Alexandris, G., Moralis-Pegios, M., Pleros, N., Tefas, A.

    Published in Neural networks (01-11-2022)
    “…Recent advances in Deep Learning (DL) fueled the interest in developing neuromorphic hardware accelerators that can improve the computational speed and energy…”
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    Journal Article
  4. 4

    Training Noise-Resilient Recurrent Photonic Networks for Financial Time Series Analysis by Passalis, N., Kirtas, M., Mourgias-Alexandris, G., Dabos, G., Pleros, N., Tefas, A.

    “…Photonic-based neuromorphic hardware holds the credentials for providing fast and energy efficient implementations of computationally complex Deep Learning…”
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    Conference Proceeding
  5. 5

    Normalized Post-training Quantization for Photonic Neural Networks by Kirtas, M., Passalis, N., Oikonomou, A., Mourgias-Alexandris, G., Moralis-Pegios, M., Pleros, N., Tefas, A.

    “…The recent advances of Deep Learning (DL) have fueled the research of hardware accelerators, which can signif-icantly improve the computation speed and energy…”
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    Conference Proceeding
  6. 6

    Combustion dynamics in a high aspect ratio engine by Kirtas, M., Disseau, M., Scarborough, D., Jagoda, J., Menon, S.

    “…A large eddy simulation (LES) based design tool for modeling rectangular cross-section, high aspect ratio (AR) combustors has been developed and tested against…”
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    Journal Article
  7. 7

    Programmable Tanh-, ELU-, Sigmoid-, and Sin-based Nonlinear Activation Functions for Neuromorphic Photonics by Pappas, C., Kovaios, S., Moralis-Pegios, M., Tsakyridis, A., Giamougiannis, G., Kirtas, M., Kerrebrouck, J. Van, Coudyzer, G., Yin, X., Passalis, N., Tefas, A., Pleros, N.

    “…We demonstrate a programmable analog opto-electronic (OE) circuit that can be configured to provide a range of nonlinear activation functions for incoherent…”
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    Journal Article
  8. 8

    Photonic Neural Networks and Optics-informed Deep Learning Fundamentals by Tsakyridis, A, Moralis-Pegios, M, Giamougiannis, G, Kirtas, M, Passalis, N, Tefas, A, Pleros, N

    Published 22-11-2023
    “…The recent explosive compute growth, mainly fueled by the boost of AI and DNNs, is currently instigating the demand for a novel computing paradigm that can…”
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    Journal Article
  9. 9

    Learning photonic neural network initialization for noise-aware end-to-end fiber transmission by Kirtas, M., Passalis, N., Mourgias-Alexandris, G., Dabos, G., Pleros, N., Tefas, A.

    “…Deep Learning (DL) has dominated a wide range of applications due to its state-of-the-art performance. Novel approaches introduce Artificial Neural Networks…”
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    Conference Proceeding
  10. 10

    Optics-informed Neural Networks: Bridging Deep Learning with Photonic Accelerators by Moralis-Pegios, M., Tsakyridis, A., Pappas, C., Moschos, T., Giamougiannis, G., Kovaios, S., Roumpos, I., Kirtas, M., Passalis, N., Tefas, A., Pleros, N.

    “…We discuss our work in optics informed photonic neural networks, an architectural framework bridging the idiosyncrasy of integrated photonic architectures with…”
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    Conference Proceeding
  11. 11

    Early Detection of DDoS Attacks using Photonic Neural Networks by Kirtas, M., Passalis, N., Kalavrouziotis, D., Syrivelis, D., Bakopoulos, P., Pleros, N., Tefas, A.

    “…Deep Learning (DL) has been extensively used in challenging tasks including security applications such as Distributed Denial of Service (DDoS) attacks…”
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    Conference Proceeding
  12. 12

    Physics-inspired End-to-End Deep Learning for High-Performance Optical Fiber Transmission Links by Roumpos, I., De Marinis, L., Mourgias-Alexandris, G., Kirtas, M., Passalis, N., Tefas, A., Contestabile, G., Vyrsokinos, K., Pleros, N., Moralis-Pegios, M.

    “…We experimentally demonstrate the performance improvements obtained through End-to-End Deep Learning in noise and chromatic dispersion compensation of optical…”
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    Conference Proceeding