Photonic convolutional reservoir computing based on VCSEL with multiple optical injections

Abstract

In this paper, a photonic convolutional time delay reservoir computing (C-TD-RC) system based on vertical cavity surface emitting laser (VCSEL) with multiple optical injections has been proposed. Here, the convolutional neural networks (CNNs) and the time delay reservoir computing (TD-RC) are combined in the system. Using TD-RC instead of the fully connected layer in the traditional CNNs can solve the problem of gradient vanishing and gradient explosion in CNNs. Two classification tasks including handwritten digits recognition and spoken digits classification are solved by the C-TD-RC system. In addition, in order to achieve better classification performance of RC, a new post-processing method combining ridge regression and winner take all strategy has been adopted. Numerical results demonstrate that the minimum Word Error Rate (WER) can reach 0.0135 for handwritten digits recognition and 0.0213 for spoken digits classification tasks by the C-TD-RC system. Furthermore, the effects of the virtual nodes, the injection paths and the number of convolution kernels applied in the first and second convolution layers on the C-TD-RC system are considered. The proposed C-TD-RC scheme is of great significance to the performance enhancement of RC in the future.