Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks

@article{Pai2022ExperimentallyRI,
  title={Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks},
  author={Sunil Pai and Zhanghao Sun and Tyler W. Hughes and Taewon Park and Ben Bartlett and Ian A. D. Williamson and Momchil Minkov and Maziyar Milanizadeh and Nathnael Abebe and Francesco Morichetti and Andrea Melloni and Shanhui Fan and Olav Solgaard and David A. B. Miller},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.08501}
}
Neural networks are widely deployed models across many scientific disciplines and commercial endeavors ranging from edge computing and sensing to large-scale signal processing in data cen-ters. The most efficient and well-entrenched method to train such networks is backpropagation, or reverse-mode automatic differentiation. To counter an exponentially increasing energy budget in the artificial intelligence computing sector, there has been recent interest in analog implementations of neural networks… 
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