Reconfigurable Integrated Photonic Unitary Neural Networks With Phase Encoding Enabled by In-Situ Training

Reconfigurable Integrated Photonic Unitary Neural Networks With Phase Encoding Enabled by In-Situ Training

Photonic neural networks are emerging as promising computing platforms for artificial intelligence (AI). Particularly, integrated photonic unitary neural networks (IPUNNs) are capable of mitigating gradient vanishing/explosion problems when deeper neural networks are constructed. Furthermore, their...

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Bibliographic Details
Main Authors: Shengjie Tang, Cheng Chen, Qi Qin, Xiaoping Liu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Photonics Journal
Subjects:
In-situ training
phase encoding
photonic unitary neural network
reconfigurable
Online Access:https://ieeexplore.ieee.org/document/10663838/
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