Archivio Istituzionale della RicercaWe simulated tensor-train decomposed neural networks realized by Mach-Zehnder interferometer-based scalable photonic neuromorphic devices. The simulation results demonstrate that under practical hardware imprecisions, the TT-decomposed neural networks can achieve >90% test accuracy with 33.6× fewer MZIs than conventional photonic neural network implementations.
Analysis of the Hardware Imprecisions for Scalable and Compact Photonic Tensorized Neural Networks / On, M. B.; Lee, Y. -J.; Xiao, X.; Proietti, R.; Ben Yoo, S. J.. - ELETTRONICO. - (2021), pp. 1-4. (Intervento presentato al convegno 2021 European Conference on Optical Communication, ECOC 2021 tenutosi a Bordeaux, France nel 13-16 September 2021) [10.1109/ECOC52684.2021.9605948].
Analysis of the Hardware Imprecisions for Scalable and Compact Photonic Tensorized Neural Networks
Proietti R.;
2021
Abstract
We simulated tensor-train decomposed neural networks realized by Mach-Zehnder interferometer-based scalable photonic neuromorphic devices. The simulation results demonstrate that under practical hardware imprecisions, the TT-decomposed neural networks can achieve >90% test accuracy with 33.6× fewer MZIs than conventional photonic neural network implementations.
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ECOC2021_TPNN_simulation_v6.pdf
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https://hdl.handle.net/11583/2973263