Unconventional Computing based on Four Wave Mixing in Highly Nonlinear Waveguides
This work addresses the need for efficient, low-power nonlinear processing in optical communications and computing, representing an incremental advancement in photonic unconventional computing.
The authors tackled the problem of performing nonlinear signal processing directly in the optical domain by proposing a photonic accelerator based on four-wave mixing in highly nonlinear waveguides, achieving results superior to strong machine learning algorithms in all-optical nonlinearity compensation with reduced power consumption and computational complexity.
In this work we numerically analyze a photonic unconventional accelerator based on the four-wave mixing effect in highly nonlinear waveguides. The proposed scheme can act as a fully analogue system for nonlinear signal processing directly in the optical domain. By exploiting the rich Kerr-induced nonlinearities, multiple nonlinear transformations of an input signal can be generated and used for solving complex nonlinear tasks. We first evaluate the performance of our scheme in the Santa-Fe chaotic time-series prediction. The true power of this processor is revealed in the all-optical nonlinearity compensation in an optical communication scenario where we provide results superior to those offered by strong machine learning algorithms with reduced power consumption and computational complexity. Finally, we showcase how the FWM module can be used as a reconfigurable nonlinear activation module being capable of reproducing characteristic functions such as sigmoid or rectified linear unit.