Parity time symmetric optical neural networks

Optical neural networks (ONNs), implemented on an array of cascaded Mach-Zehnder interferometers (MZIs), have recently been proposed as a possible replacement for conventional deep learning hardware. They potentially offer higher energy efficiency and computational speed when compared to their electronic counterparts. By utilizing tunable phase shifters, one can adjust the output of each of MZI to enable emulation of arbitrary matrix-vector multiplication. These phase shifters are central to the programmability of ONNs, but they require a large footprint and are relatively slow. Here we propose an ONN architecture that utilizes parity-time (PT) symmetric couplers as its building blocks. Instead of modulating phase, gain-loss contrasts across the array are adjusted as a means to train the network. We demonstrate that PT symmetric ONNs (PT-ONNs) are adequately expressive by performing the digit-recognition task on the Modified National Institute of Standards and Technology dataset. Compared to conventional ONNs, the PT-ONN achieves a comparable accuracy (67% versus 71%) while circumventing the problems associated with changing phase. Our approach may lead to new and alternative avenues for fast training in chip-scale ONNs. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

A new architecture of optical neural networks (ONNs) utilizing parity-time (PT) symmetric couplers as building blocks has been proposed, with gain-loss contrasts adjusted for training the network to avoid problems associated with changing phase. The PT symmetric ONNs show adequate expressiveness in tasks like digit recognition and achieve comparable accuracy to conventional ONNs. This approach may lead to new avenues for fast training in chip-scale ONNs.