Handling multilayer neural network nonlinear equalizer complexity and overfitting challenges using L1-regularization for 112Gbps optical interconnects

Govind Sharan Yadav; Chun-Yen Chuang; Kai-Ming Feng; Jyehong Chen; Young-Kai Chen

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Handling multilayer neural network nonlinear equalizer complexity and overfitting challenges using L1-regularization for 112Gbps optical interconnects

Govind Sharan Yadav, Chun-Yen Chuang, Kai-Ming Feng, Jyehong Chen and Young-Kai Chen

Optics InfoBase Conference Papers, S3A.3

2021

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Electronic

Optical and Magnetic Materials

Mechanics of Materials

We propose an L1-regularized multilayer neural network nonlinear equalizer (L1PMLNLE) for inter-data-center interconnects. Compared with conventional and sparse VNLE, the L1PML-NLE reduces 81% and 57.8% complexity with improved BER performance for 40-km 112-Gb/s PAM4 transmission.

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Title: Handling multilayer neural network nonlinear equalizer complexity and overfitting challenges using L1-regularization for 112Gbps optical interconnects
Translated title: Handling multilayer neural network nonlinear equalizer complexity and overfitting challenges using L1-regularization for 112Gbps optical interconnects
Creators - without role: Govind Sharan Yadav - Institute of Photonics Technologies , National Tsing Hua University
Chun-Yen Chuang - Department of Photonics , National Chiao Tung University
Kai-Ming Feng - Institute of Photonics Technologies , National Tsing Hua University
Jyehong Chen - Department of Photonics , National Chiao Tung University
Young-Kai Chen - DARPA
Identifiers: 9781557528209; 9957773192106774
Academic Unit: Institute of Photonics Technologies, College of Electrical Engineering and Computer Science, National Tsing Hua University
Language: English
Resource Type: Conference paper
Publication Details: Optics InfoBase Conference Papers, S3A.3

Handling multilayer neural network nonlinear equalizer complexity and overfitting challenges using L1-regularization for 112Gbps optical interconnects

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