Optoelectronic convolutional neural networks based on time-stretch method

Abstract

In this paper, a new architecture of optoelectronic convolutional neural networks (CNNs) based on time-stretch method is proposed. In this loop-shaped structure mainly composed of fiber optical and electronic devices, computations of data from each layer of CNN which are carried by light pulses with high repetition rate can be accomplished in a serial way. Therefore, a 5-layer CNN with two convolution layers, two mean pooling layers and one fully-connected layer are implemented. Under the test of handwriting digit recognition, its accuracy can reach up to 95% under ideal circumstances. Tests under different relative noise levels have been conducted and analyzed as well.