J. R. Ong, C. C. Ooi, T. Y. L. Ang, S. T. Lim and C. E. Png, "Photonic convolutional neural networks using integrated diffractive optics," in IEEE Journal of Selected Topics in Quantum Electronics.
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.
This research is supported by core funding from: Institute of High Performance Computing
Grant Reference no. : SC23-R0005