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Accelerating 56G PAM4 Link Equalization Optimization Using Machine Learning-based Analysis

Ting Zhu (Senior Hardware Engineer, Hewlett Packard Enterprise)

Yongjin Choi (Master Technologist, Hewlett Packard Enterprise)

Christopher Cheng (Distinguished Technologist, Hewlett Packard Enterprise)

Jacky Chang (Distinguished Technologist, Hewlett Packard Enterprise Company)

Location: Ballroom G

Date: Thursday, January 31

Time: 8:00am - 8:45am

Track: 15. Machine Learning for Microelectronics, Signaling & System Design, 08. Optimizing High-Speed Serial Design

Session Type: Technical Session

Vault Recording: TBD

Audience Level: All

Adaptive equalizers are widely applied to improve signal integrity in high-speed communication systems. For the links with multiple adaptive equalizers in transmitter and receiver, it is challenging for the tuning algorithms to handle high-dimensional adaptive parameters and to converge within the limited training time. One method to accelerate the convergence is to reduce the tuning dimensions. In this paper, we proposed a new method to reduce the tuning dimensions through machine learning-based Principal Component Analysis (PCA). It uses the link bit-error-rate (BER) for analysis and generates principle tuning vectors. The method is demonstrated in a 56G PAM4 link example.

Takeaway

This paper proposed a new method to reduce the tuning dimensions of adaptive equalizers through principal component analysis. Detailed method, working flow and the demo examples will be discussed.

Presentation Files

SLIDES_15_Accelerating56GPAM4LinkEqualization_Zhu.pdf
PAPER_15_Accelerating56GPAM4LinkEqualization_Zhu.pdf