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A COM Based High Speed Serial Link Optimization Using Machine Learning
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This paper presents a channel operating margin (COM) based high-speed serial link optimization using machine learning (ML). COM that is proposed for evaluating serial link is calculated at first and during the calculation several important equalization parameters corresponding to the best configuration are extracted which can be used for the ML modeling of serial link. Then a deep neural network containing hidden layers are investigated to model a whole serial link equalization including transmitter feed forward equalizer (FFE), receiver continuous time linear equalizer (CTLE) and decision feedback equalizer (DFE). By training, validating and testing a lot of samples that meet the COM specification of 400GAUI-8 C2C, an effective ML model is generated and the maximum relative error is only 0.1 compared with computation results. At last 3 link configurations are discussed from the view of tradeoff between the link performance and cost, illustrating that our COM based ML modeling method can be applied to advanced serial link design for NRZ, PAM4 or even other higher level pulse amplitude modulation signal.
- Publication
- IEICE TRANSACTIONS on Electronics Vol.E105-C No.11 pp.684-691
- Publication Date
- 2022/11/01
- Publicized
- 2022/05/09
- Online ISSN
- 1745-1353
- DOI
- 10.1587/transele.2021ESP0003
- Type of Manuscript
- Special Section PAPER (Special Section on Recent Advances in Simulation Techniques and Their Applications for Electronics)
- Category
Authors
Yan WANG
Southeast University
Qingsheng HU
Southeast University
Keyword
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Yan WANG, Qingsheng HU, "A COM Based High Speed Serial Link Optimization Using Machine Learning" in IEICE TRANSACTIONS on Electronics,
vol. E105-C, no. 11, pp. 684-691, November 2022, doi: 10.1587/transele.2021ESP0003.
Abstract: This paper presents a channel operating margin (COM) based high-speed serial link optimization using machine learning (ML). COM that is proposed for evaluating serial link is calculated at first and during the calculation several important equalization parameters corresponding to the best configuration are extracted which can be used for the ML modeling of serial link. Then a deep neural network containing hidden layers are investigated to model a whole serial link equalization including transmitter feed forward equalizer (FFE), receiver continuous time linear equalizer (CTLE) and decision feedback equalizer (DFE). By training, validating and testing a lot of samples that meet the COM specification of 400GAUI-8 C2C, an effective ML model is generated and the maximum relative error is only 0.1 compared with computation results. At last 3 link configurations are discussed from the view of tradeoff between the link performance and cost, illustrating that our COM based ML modeling method can be applied to advanced serial link design for NRZ, PAM4 or even other higher level pulse amplitude modulation signal.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/transele.2021ESP0003/_p
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@ARTICLE{e105-c_11_684,
author={Yan WANG, Qingsheng HU, },
journal={IEICE TRANSACTIONS on Electronics},
title={A COM Based High Speed Serial Link Optimization Using Machine Learning},
year={2022},
volume={E105-C},
number={11},
pages={684-691},
abstract={This paper presents a channel operating margin (COM) based high-speed serial link optimization using machine learning (ML). COM that is proposed for evaluating serial link is calculated at first and during the calculation several important equalization parameters corresponding to the best configuration are extracted which can be used for the ML modeling of serial link. Then a deep neural network containing hidden layers are investigated to model a whole serial link equalization including transmitter feed forward equalizer (FFE), receiver continuous time linear equalizer (CTLE) and decision feedback equalizer (DFE). By training, validating and testing a lot of samples that meet the COM specification of 400GAUI-8 C2C, an effective ML model is generated and the maximum relative error is only 0.1 compared with computation results. At last 3 link configurations are discussed from the view of tradeoff between the link performance and cost, illustrating that our COM based ML modeling method can be applied to advanced serial link design for NRZ, PAM4 or even other higher level pulse amplitude modulation signal.},
keywords={},
doi={10.1587/transele.2021ESP0003},
ISSN={1745-1353},
month={November},}
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TY - JOUR
TI - A COM Based High Speed Serial Link Optimization Using Machine Learning
T2 - IEICE TRANSACTIONS on Electronics
SP - 684
EP - 691
AU - Yan WANG
AU - Qingsheng HU
PY - 2022
DO - 10.1587/transele.2021ESP0003
JO - IEICE TRANSACTIONS on Electronics
SN - 1745-1353
VL - E105-C
IS - 11
JA - IEICE TRANSACTIONS on Electronics
Y1 - November 2022
AB - This paper presents a channel operating margin (COM) based high-speed serial link optimization using machine learning (ML). COM that is proposed for evaluating serial link is calculated at first and during the calculation several important equalization parameters corresponding to the best configuration are extracted which can be used for the ML modeling of serial link. Then a deep neural network containing hidden layers are investigated to model a whole serial link equalization including transmitter feed forward equalizer (FFE), receiver continuous time linear equalizer (CTLE) and decision feedback equalizer (DFE). By training, validating and testing a lot of samples that meet the COM specification of 400GAUI-8 C2C, an effective ML model is generated and the maximum relative error is only 0.1 compared with computation results. At last 3 link configurations are discussed from the view of tradeoff between the link performance and cost, illustrating that our COM based ML modeling method can be applied to advanced serial link design for NRZ, PAM4 or even other higher level pulse amplitude modulation signal.
ER -
English
