On-Demand Reconfigurable Incoherent Optical Matrix Operator for Real-Time Video Image Display

Abstract

For emerging application fields such as autonomous driving and computing vision, which demands the real-time response of massive information, advanced image processing techniques on artificial neural networks (ANNs) are crucial. However, with increasing task complexity, the electrical hardware limited by the well-known von Neumann bottleneck is inadequate to meet these extreme requirements in computing speed and computing power. We propose an incoherent optical matrix operator by dynamically manipulating the parameters of the lightwave in terms of the wavelength, time and space to implement the on-demand reconfiguration both in the optical perceptron and optical convolutional processor. For the optical perceptron, it is conducted to perform the case-sensitive classification of 52 English letters. Furthermore, by adjusting the spectral characteristics and output ports, an optical convolution processor is constructed to realize multitask convolution processing for both the image and real-time video. Based on multikernels parallel computing in an optical convolutional layer coupled with an electrically fully connected layer, a convolutional neural network is built to recognize ‘0–9’ handwritten digital images from the Modified National Institute of Standards and Technology (MNIST) database. Owing to the excellent performance of the incoherent beam in terms of the spectral purity, wavelength stability and side-mode suppression ratio, the experimental classification accuracy up to 96.01% greatly matches the theoretical value of 96.67%. Moreover, incoherent information processing is synchronous with data transmission, which releases the requirement of the global clock in digital counterparts. The high-fidelity incoherent convolution processor offers a novel computing framework that serves the next-generation ANN platform.