Compressed Neural Network Equalization Based on Iterative Pruning Algorithm for 112-Gbps VCSEL-Enabled Optical Interconnects
Ling Ge, Wenjia Zhang, Chenyu Liang, Zuyuan He
Abstract
Advanced nonlinear digital signal processing technologies, which bring significant performance gain for high-speed optical interconnects, are highly constrained by huge complexity in the actual deployment. Fully connected neural network-based equalizer has shown powerful efficacy to deal with the complex linear and nonlinear impairments for VCSEL-enabled multi-mode optical interconnects, but it also contains a number of redundancies with little impact on performance improvement. In this article, we experimentally demonstrate a compressed neural network equalization using the iterative pruning algorithm for 112-Gbps VCSEL-enabled PAM-4 and PAM-8 transmissions over 100-m MMF. We also study the impact of threshold and pruning span on the performance of proposed algorithms. The results show up to 71% connection compression by use of the iterative pruning algorithms and maximum 28.4% improvement compared with the one-shot pruning algorithm.