Abstract

Hardware implementation of neural network based nonlinear equalizers will encounter tremendous challenges due to a high-throughput data stream and high computational complexity for 100-Gbps short-reach optical interconnects. In this Letter, we propose a parallel pruned neural network equalizer for high-throughput signal processing and minimized hardware resources. The structure of a time-interleaved neural network equalizer with a delay module is deployed in a field programmable gate array with advanced pruned algorithms, demonstrating significant bit error rate reduction for 100-Gbps real-time throughput with 200 parallel channels. Moreover, the dependence of processing throughput, hardware resources, and equalization performance is investigated, showing that over 50% resource reduction without performance degradation can be achieved with the pruning strategy.

© 2021 Optical Society of America

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