A tunable neural network based decision feed-back equalizer model for high-speed link simulation

Thong Nguyen; Jose Schutt-Aine

doi:10.1109/EPEPS48591.2020.9231402

A tunable neural network based decision feed-back equalizer model for high-speed link simulation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents a model combining a feedforward neural network (FNN) with a recurrent neural network (RNN) to model Decision Feed-back Equalizer (DFE). By using the FNN as the mapping between the tap values and the dynamic behavior of the DFE, a complete model of the DFE can be constructed for channel simulation. The paper shows a 2-tap DFE example in which excellent agreement between the model generated by the proposed method and transient simulation can be observed.

Original language	English (US)
Title of host publication	EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728161617
DOIs	https://doi.org/10.1109/EPEPS48591.2020.9231402
State	Published - Oct 2020
Event	29th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2020 - San Jose, United States Duration: Oct 5 2020 → Oct 7 2020

Publication series

Name	EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems

Conference

Conference	29th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2020
Country/Territory	United States
City	San Jose
Period	10/5/20 → 10/7/20

ASJC Scopus subject areas

Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials
Energy Engineering and Power Technology

Online availability

10.1109/EPEPS48591.2020.9231402

Library availability

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Cite this

Nguyen, T., & Schutt-Aine, J. (2020). A tunable neural network based decision feed-back equalizer model for high-speed link simulation. In EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems Article 9231402 (EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EPEPS48591.2020.9231402

A tunable neural network based decision feed-back equalizer model for high-speed link simulation. / Nguyen, Thong; Schutt-Aine, Jose.
EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc., 2020. 9231402 (EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nguyen, T & Schutt-Aine, J 2020, A tunable neural network based decision feed-back equalizer model for high-speed link simulation. in EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems., 9231402, EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems, Institute of Electrical and Electronics Engineers Inc., 29th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2020, San Jose, United States, 10/5/20. https://doi.org/10.1109/EPEPS48591.2020.9231402

Nguyen T, Schutt-Aine J. A tunable neural network based decision feed-back equalizer model for high-speed link simulation. In EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc. 2020. 9231402. (EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems). doi: 10.1109/EPEPS48591.2020.9231402

Nguyen, Thong ; Schutt-Aine, Jose. / A tunable neural network based decision feed-back equalizer model for high-speed link simulation. EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc., 2020. (EPEPS 2020 - IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems).

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abstract = "This paper presents a model combining a feedforward neural network (FNN) with a recurrent neural network (RNN) to model Decision Feed-back Equalizer (DFE). By using the FNN as the mapping between the tap values and the dynamic behavior of the DFE, a complete model of the DFE can be constructed for channel simulation. The paper shows a 2-tap DFE example in which excellent agreement between the model generated by the proposed method and transient simulation can be observed. ",

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note = "Funding Information: ACKNOWLEDGEMENT This material is based upon work supported by the National Science Foundation under Grant No. CNS 16-24810 for Center for Advanced Electronics through Machine Learning (CAEML), the U.S Army Small Business Innovation Research (SBIR) Program office and the U.S. Army Research Office under Contract No.W911NF-16-C-0125. Publisher Copyright: {\textcopyright} 2020 IEEE.; 29th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2020 ; Conference date: 05-10-2020 Through 07-10-2020",

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AB - This paper presents a model combining a feedforward neural network (FNN) with a recurrent neural network (RNN) to model Decision Feed-back Equalizer (DFE). By using the FNN as the mapping between the tap values and the dynamic behavior of the DFE, a complete model of the DFE can be constructed for channel simulation. The paper shows a 2-tap DFE example in which excellent agreement between the model generated by the proposed method and transient simulation can be observed.

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A tunable neural network based decision feed-back equalizer model for high-speed link simulation

Abstract

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ASJC Scopus subject areas

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