A Convolution Neural Network Implemented by Three 3 × 3 Photonic Integrated Reconfigurable Linear Processors

Abstract

The convolution neural network (CNN) is a classical neural network with advantages in image processing. The use of multiport optical interferometric linear structures in neural networks has recently attracted a great deal of attention. Here, we use three 3 × 3 reconfigurable optical processors, based on Mach-Zehnder interferometers (MZIs), to implement a two-layer CNN. To circumvent the random phase errors originating from the fabrication process, MZIs are calibrated before the classification experiment. The MNIST datasets and Fashion-MNIST datasets are used to verify the classification accuracy. The optical processor achieves 86.9% accuracy on the MNIST datasets and 79.3% accuracy on the Fashion-MNIST datasets. Experiments show that we can improve the classification accuracy by reducing phase errors of MZIs and photodetector (PD) noises. In the future, our work provides a way to embed the optical processor in CNN to compute matrix multiplication.