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.

本研究提出一种高性能的基于人工神经网络的均衡器的 FPGA 实现，以满足现代光通信系统的吞吐量要求，并通过变化的并行度度量体现高度灵活性。该实现在光纤通道的比特误码率（BER）上相比传统方法降低了四倍，同时在类似批处理大小下超过 40 GBd 的吞吐量上相比高性能图形处理单元（GPU）提高了三个数量级。