  To satisfy the growing throughput demand of data-intensive applications, the
performance of optical communication systems increased dramatically in recent
years. With higher throughput, more advanced equalizers are crucial, to
compensate for impairments caused by inter-symbol interference (ISI). The
latest research shows that artificial neural network (ANN)-based equalizers are
promising candidates to replace traditional algorithms for high-throughput
communications. On the other hand, not only throughput but also flexibility is
a main objective of beyond-5G and 6G communication systems. A platform that is
able to satisfy the strict throughput and flexibility requirements of modern
communication systems are field programmable gate arrays (FPGAs). Thus, in this
work, we present a high-performance FPGA implementation of an ANN-based
equalizer, which meets the throughput requirements of modern optical
communication systems. Further, our architecture is highly flexible since it
includes a variable degree of parallelism (DOP) and therefore can also be
applied to low-cost or low-power applications which is demonstrated for a
magnetic recording channel. The implementation is based on a cross-layer design
approach featuring optimizations from the algorithm down to the hardware
architecture, including a detailed quantization analysis. Moreover, we present
a framework to reduce the latency of the ANN-based equalizer under given
throughput constraints. As a result, the bit error ratio (BER) of our equalizer
for the optical fiber channel is around four times lower than that of a
conventional one, while the corresponding FPGA implementation achieves a
throughput of more than 40 GBd, outperforming a high-performance graphics
processing unit (GPU) by three orders of magnitude for a similar batch size.
