Computationally-Efficient Sparsely-Connected Multi-Output Neural Networks for IM/DD System Equalization (W2A.26)
Presenter: Zhaopeng Xu, University of Melbourne
Low-complexity sparsely-connected multi-output neural networks are proposed for equalization in a 50-Gb/s 25-km PAM4 IM/DD system. Compared with traditional fully-connected single-output counterparts, a gross complexity reduction of 60.4%/56.7% can be achieved with 2-layer FNN/C-FNN architecture.
Authors:Zhaopeng Xu, University of Melbourne / Shuangyu Dong, University of Melbourne / Honglin Ji, University of Melbourne / Jonathan Manton, University of Melbourne / William Shieh, University of Melbourne