  For the first time, recurrent and feedforward neural network-based equalizers
for nonlinearity compensation are implemented in an FPGA, with a level of
complexity comparable to that of a dispersion equalizer. We demonstrate that
the NN-based equalizers can outperform a 1 step-per-span DBP.
