Optical ReLU-like activation function based on a semiconductor laser with optical injection
Guanting Liu, Yi-Wei Shen, Ruiqian Li, Jingyi Yu, Xuming He, Cheng Wang
Abstract
Artificial neural networks usually consist of successive linear multiply-accumulate operations and nonlinear activation functions. However, most optical neural networks only achieve the linear operation in the optical domain, while the optical implementation of activation function remains challenging. Here we present an optical ReLU-like activation function (with 180° rotation) based on a semiconductor laser subject to the optical injection in an experiment. The ReLU-like function is achieved in a broad regime above the Hopf bifurcation of the injection-locking diagram and is operated in the continuous-wave mode. In particular, the slope of the activation function is reconfigurable by tuning the frequency difference between the master laser and the slave laser.