DesignCon is part of the Informa Markets Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them. Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Early Bird Registration Now Open till November 30th. Save Up to $300 Today!

DesignCon 2019 Presentation Viewer

Purchase procecdings

Welcome to the DesignCon Presentation Store. Here you can view and download conference and/or show floor theater presentations before, during, and after the event. If you’re looking for a presentation from a specific session that you’re unable to find here, note that it’s likely because the presenter has not provided permission for external use or has not yet shared their presentation with us. Please check back after the event for a more complete catalogue of available presentations.

If you’d like to do a bulk download of all conference presentations or technical papers at once, please click here for conference presentations or click here for full technical papers. For sessions not included in the main conference, click here for Chiphead Theater presentations or click here for sponsored session presentations.

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.


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