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Self-Evolution Cascade Deep Learning for SerDes Adaptive Equalization

Bowen Li  (PhD candidate, North Carolina State University)

Brandon Jiao  (Senior Staff Transceiver Engineer, Xilinx, Inc.)

Chih-Hsun Chou  (Xilinx Inc.)

Romi Mayder  (Senior Director, Xilinx, Inc.)

Paul Franzon  (Distinguished Professor, North Carolina State University)

Geoff (Geoffrey) Zhang  (Distinguished Engineer, Xilinx, Inc.)

Location: Ballroom G

Date: Thursday, January 30

Time: 2:50pm - 3:30pm

Track: 14. Machine Learning for Microelectronics, Signaling & System Design, 06. System Co-Design: Modeling, Simulation & Measurement Validation

Format: Technical Session

Pass Type: 2-Day Pass, All-Access Pass, Alumni All-Access Pass - Get your pass now!

Vault Recording: TBD

Audience Level: All

IBIS-AMI technology has become the de-facto methodology to model SerDes behavior for end-to-end link simulations. Meanwhile, machine learning (ML) techniques can facilitate the computer to learn a black-box system. This paper proposes the Self-Evolution Cascade Deep Learning (SCDL) model to show a parallel approach to modeling adaptive SerDes behavior effectively. Specifically, the proposed self-guide learning methodology uses its own failure experiences to optimize its future solution search according to the prediction of the receiver equalization adaptation trend. After the basic introduction of SCDL model, the paper presents results that are highly correlated with the IBIS-AMI simulations.


With self-guide information, the proposed SCDL model can accurately track the CTLE and DFE equalization adaptations over 50 various cases within 20 seconds based on only 150 training cases.

Intended Audience

1. Basic concept of high-speed serial link systems
2. Basic idea of IBIS-AMI modeling of high-speed serial links
3. Basic understanding system performance evaluation and analysis
4. Basic knowledge of machine learning/deep learning algorithms

Presentation Files