Training a neural network with exciton-polariton optical nonlinearity

Andrzej Opala; Riccardo Panico; Vincenzo Ardizzone; Barbara Pietka; Jacek Szczytko; Daniele Sanvitto; Michał Matuszewski; Dario Ballarini

arXiv · 2107.11156

Training a neural network with exciton-polariton optical nonlinearity

Andrzej Opala·Riccardo Panico·Vincenzo Ardizzone·Barbara Pietka·Jacek Szczytko·Daniele Sanvitto·Michał Matuszewski·Dario Ballarini

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Abstract

In contrast to software simulations of neural networks, hardware implementations have often limited or no tunability. While such networks promise great improvements in terms of speed and energy efficiency, their performance is limited by the difficulty to apply efficient training. We propose and realize experimentally an optical system where highly efficient backpropagation training can be applied through an array of highly nonlinear, non-tunable nodes. The system includes exciton-polariton nodes realizing nonlinear activation functions. We demonstrate a high classification accuracy in the MNIST handwritten digit benchmark in a single hidden layer system.

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Andrzej Opala, Riccardo Panico, Vincenzo Ardizzone, Barbara Pietka, Jacek Szczytko, Daniele Sanvitto, Michał Matuszewski, Dario Ballarini. 2021-07-23. Training a neural network with exciton-polariton optical nonlinearity. https://arxiv.org/abs/2107.11156

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