Computationally-Efficient Sparsely-Connected Multi-Output Neural Networks for IM/DD System Equalization
2022 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXHIBITION (OFC)（2022）
Abstract
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. (C) 2022 The Author(s)
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Key words
computationally-efficient sparsely-connected multioutput neural networks,low-complexity sparsely-connected multioutput neural networks,PAM4 IM-DD system,fully-connected single-output counterparts,2-layer FNN-C-FNN architecture,feedforward neural network,intensity-modulated directly-detected systems
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