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

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 x 10(11) multiplications and accumulations per second (MACs/s) and a multiplication of a 4 x 4 matrix and a 4 x 1 vector at 2.69 x 10(9) MACs/s. The edge extraction of 32 x 32 binary images is also realized in simulation by optical 2D convolution at 5 x 10(8) 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. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement