Abstract
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.