  With recent rapid advances in photonic integrated circuits, it has been
demonstrated that programmable photonic chips can be used to implement
artificial neural networks. Convolutional neural networks (CNN) are a class of
deep learning methods that have been highly successful in applications such as
image classification and speech processing. We present an architecture to
implement a photonic CNN using the Fourier transform property of integrated
star couplers. We show, in computer simulation, high accuracy image
classification using the MNIST dataset. We also model component imperfections
in photonic CNN and show that the performance degradation can be recovered in a
programmable chip. Our proposed architecture provides a large reduction in
physical footprint compared to current implementations as it utilizes the
natural advantages of optics and hence offers a scalable pathway towards
integrated photonic deep learning processors.
