Time-stretch optical neural network with time-division multiplexing

Abstract

As factors such as power consumption and bandwidth constrain the development of electronic computing in deep learning, optical neuromorphic computing has the potential to meet the growing computational demands of artificial intelligence, presenting various opportunities and challenges. In this paper, we propose an all-optical neural network through a combination of time-stretching and time-division multiplexing that can be used for the classification of multiple datasets. The system modulates a large number of parameters into chirp optical pulses by time-stretching and increases operational efficiency in conjunction with time-division multiplexing. This all-optical neural network system proposed is capable of operating at the speed of 1 Tera-OPS/s and can achieve data transfer rate in excess of 800 Gbit/s. The system is tested by the feedforward neural network, and the computational simulation realized very encouraging and considerable results.