Structured sparsity learning-based pruned retraining volterra equalization for data-center interconnects

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

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Structured sparsity learning-based pruned retraining volterra equalization for data-center interconnects

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

Optics InfoBase Conference Papers

2021

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Optical and Magnetic Materials

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We propose a structured sparsity learning-based pruned retraining Volterra equalization for inter-dadta-center interconnects. Compared with conventional VE, we achieve 95% and 90.5% complexity reduction without signal degradation for B2B and 40-km at 80-Gb/s PAM4, respectively.

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Title: Structured sparsity learning-based pruned retraining volterra equalization for data-center interconnects
Translated title: Structured sparsity learning-based pruned retraining volterra equalization for data-center 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
Publication Details: Optics InfoBase Conference Papers
Identifiers: 9781557528209; 9957771948406774
Academic Unit: Institute of Photonics Technologies, College of Electrical Engineering and Computer Science, National Tsing Hua University
Language: English
Resource Type: Conference paper

Structured sparsity learning-based pruned retraining volterra equalization for data-center interconnects

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