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

Abstract

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 optical implementations are also much simpler compared to non-unitary counterparts. Meanwhile, real-valued datasets still dominate AI research and the encoding strategy is critical for IPUNNs' performances. However, there are few studies to compare different encoding strategies of IPUNNs to represent these real-valued datasets and their impacts on IPUNNs' performances. Here, in the scope of encoding strategies for real-valued features, we first compare different schemes, such as phase, amplitude and hybrid encoding using numerical simulations, with benchmarks of decision boundary and image recognition tasks. These encoding strategies of IPUNNs are also compared to non-unitary real-valued neural networks (RVNNs) with trainable biases for the same benchmarks. The results suggest that phase encoding outperforms amplitude and hybrid encoding, and exhibits comparable performances to non-unitary RVNNs. To verify the numerical results, a 10×10 IPUNN chip is designed and fabricated. The phase encoding is chosen to be implemented because of its superior performances in numerical studies. We reconfigure the IPUNN chip to perform decision boundary and image recognition tasks by on-chip in-situ training. The experimental results match the simulations well. Our work provides insights for implementing reconfigurable IPUNNs in AI computing.