  In recent years, communication engineers put strong emphasis on artificial
neural network (ANN)-based algorithms with the aim of increasing the
flexibility and autonomy of the system and its components. In this context,
unsupervised training is of special interest as it enables adaptation without
the overhead of transmitting pilot symbols. In this work, we present a novel
ANN-based, unsupervised equalizer and its trainable field programmable gate
array (FPGA) implementation. We demonstrate that our custom loss function
allows the ANN to adapt for varying channel conditions, approaching the
performance of a supervised baseline. Furthermore, as a first step towards a
practical communication system, we design an efficient FPGA implementation of
our proposed algorithm, which achieves a throughput in the order of Gbit/s,
outperforming a high-performance GPU by a large margin.
