Programmable matrix operation with reconfigurable time-wavelength plane manipulation and dispersed time delay

Abstract

We propose a novel optical computing architecture for massive parallel matrix manipulation based on reconfigurable time-wavelength plane manipulation and and dispersed time delay. Two linear weighting methods in either wavelength or time domain are proposed and validated. We perform the autocorrelation function of a 7-bit m-sequence with the speed at 1.18×10¹¹ multiplications and accumulations per second (MACs/s) and a multiplication of a 4 × 4 matrix and a 4 × 1 vector at 2.69×10⁹ MACs/s. The edge extraction of 32 × 32 binary images is also realized in simulation by optical 2D convolution at 5×10⁸ MACs/s. The proposed optical computing unit can be a key building block to process complex computing tasks with advanced deep learning algorithms and it is promising for the future photonic neural network circuits.