  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.
