  The deployment of artificial neural networks-based optical channel equalizers
on edge-computing devices is critically important for the next generation of
optical communication systems. However, this is still a highly challenging
problem, mainly due to the computational complexity of the artificial neural
networks (NNs) required for the efficient equalization of nonlinear optical
channels with large dispersion-induced memory. To implement the NN-based
optical channel equalizer in hardware, a substantial complexity reduction is
needed, while we have to keep an acceptable performance level of the simplified
NN model. In this work, we address the complexity reduction problem by applying
pruning and quantization techniques to an NN-based optical channel equalizer.
We use an exemplary NN architecture, the multi-layer perceptron (MLP), to
mitigate the impairments for 30GBd 1000km transmission over a standard
single-mode fiber, and demonstrate that it is feasible to reduce the
equalizer's memory by up to 87.12%, and its complexity by up to 78.34%, without
noticeable performance degradation. In addition to this, we accurately define
the computational complexity of a compressed NN-based equalizer in the digital
signal processing (DSP) sense. Further, we examine the impact of using
different CPU and GPU settings on the power consumption and latency for the
compressed equalizer. We also verify the developed technique experimentally, by
implementing the reduced NN equalizer on two standard edge-computing hardware
units: Raspberry Pi 4 and Nvidia Jetson Nano, which are used to process the
data generated via simulating the signal's propagation down the optical-fiber
system.
