High-speed and versatile ONN through parametric-based nonlinear computation

Neural networks (NNs), especially electronic-based NNs, have been rapidly developed in the past few decades. However, the electronic-based NNs rely more on highly advanced and heavy power-consuming hardware, facing its bottleneck due to the slowdown of Moore’s law. Optical neural networks (ONNs), in which NNs are realized via optical components with information carried by photons at the speed of light, are drawing more attention nowadays. Despite the advantages of higher processing speed and lower system power consumption, one major challenge is to realize reliable and reusable algorithms in physical approaches, particularly nonlinear functions, for higher accuracy. In this paper, a versatile parametric-process-based ONN is demonstrated with its adaptable nonlinear computation realized using the highly nonlinear fiber (HNLF). With the specially designed mode-locked laser (MLL) and dispersive Fourier transform (DFT) algorithm, the overall computation frame rate can reach up to 40 MHz. Compared to ONNs using only linear computations, this system is able to improve the classification accuracies from 81.8% to 88.8% for the MNIST-digit dataset, and from 80.3% to 97.6% for the Vowel spoken audio dataset, without any hardware modifications.