Experimental realization of arbitrary activation functions for optical neural networks
Monireh Moayedi Pour Fard, Ian A. D. Williamson, Matthew Edwards, Ke Liu, Sunil Pai, Ben Bartlett, Momchil Minkov, Tyler W. Hughes, Shanhui Fan, Thien-An Nguyen
Abstract
We experimentally demonstrate an on-chip electro-optic circuit for realizing arbitrary nonlinear activation functions for optical neural networks (ONNs). The circuit operates by converting a small portion of the input optical signal into an electrical signal and modulating the intensity of the remaining optical signal. Electrical signal processing allows the activation function circuit to realize any optical-to-optical nonlinearity that does not require amplification. Such line shapes are not constrained to those of conventional optical nonlinearities. Through numerical simulations, we demonstrate that the activation function improves the performance of an ONN on the MNIST image classification task. Moreover, the activation circuit allows for the realization of nonlinearities with far lower optical signal attenuation, paving the way for much deeper ONNs.