Machine Learning for High-Speed Channel Design

Dale Becker and Jose HejaseIBM Systems

Outline

• Artificial Neural Networks and Signal Integrity

• Example: Frequency Domain Compliance Method

• Opportunity: Using HSSCDR and System Channels with the CAEML Project

• Imagine: a powerful, efficient methodology for future high-speed channel design and analysis

Genetic Algorithms Applied to SI

• EPEPSo I/O Buffers ca. 2005 to improve worst case corner prediction and

simulation timeso Bit string analysis of source synchronous eye diagrams

• IBM Applicationo Internal IBM code - SNAPo Optimization of channel parameters given a set of package models

in HSSCDR and maximization or minimization of channel responseo HSSCDR = IBM’s high speed serial simulator

• A Desire …o Develop machine learning techniques to advance the power of our

high-speed serial analysiso Leverage the machine learning fundamentals to a broad set of our

design and analysis applications

Frequency-Domain Compliance Method - Motivation

• Our initial foray into Machine Learningo Use a machine learned model to determine channel outcome

without classical time-domain eye diagram simulation of that channel

• Motivation• While beneficial in many ways time-domain simulations to

generate eye diagrams are time consuming, require specialized tools, and require high levels of expertise.

• Implementation• Develop a frequency-domain compliance method based on S-

Parameter response• Implement the method in CadenceSI

‐ S. T. Win et al., "High‐speed bus signal integrity compliance using a frequency‐domain model,"2016 IEEE International Symposium on Electromagnetic Compatibility (EMC), Ottawa, ON, 2016, pp. 29‐34.‐ S. T. Win, J. A. Hejase, W. D. Becker, G. A. Wiedemeier and D. M. Dreps, "A frequency‐domain high‐speed bus signal integrity compliance model: Design methodology and implementation," 2015 IEEE 65th Electronic Components and Technology Conference (ECTC), San Diego, CA, 2015, pp. 545‐550.

Frequency-Domain Compliance Method - Parameters

• Identify necessary channel frequency domain properties which may affect a signal travelling through the channel• Seven parameters needed for our high-speed channels

ILF

ILD

ILDB

ILDASXTF

SXTB

SXTA

Frequency-Domain Compliance Method – Model Development

• Run a large amount of full time-domain simulations on channels with variable propertieso Full HSSCDR runs including Tx/Rx IP and equalization presets

Procedure for finding the frequency domain model

Frequency-Domain Compliance Method – Application to Channel

Frequency-Domain Compliance Method – Benefits

• Reduces simulation time with less experienced users. o 10 seconds vs. multiple 15 minutes107 bit simulationo Clear pass / fail criteria with one pass of analysis

• Frequency domain compliance checking works very well to predict channel compliance with bus IO circuitry when compared to time domain simulations. o No failing channel in time domain simulation was predicted as

passing in the frequency domain compliance method Frequency domain compliance models created for 8 different

high speed buses (spanning 3 generations of IBM Server Products)

4.8 Gb/s to 25.78125 Gb/s Frequency domain compliance method is bus topology

independento Note: As more simulations are run, learned method predicts pass /

fail with less margin

Machine Learning Projects Using HSSCDR

• Ongoing work with Georgia Tech as part of CAEML

High Speed Serdes Clock Data Recovery (HSSCDR):

High Speed Serdes Clock Data Recovery (HSSCDR):• IBM tool (Troy Beukema, IBM Research)• Time domain simulation (eye diagrams / bath tub curves)• Uses Transmitter and Receiver noise, jitter and equalization properties.• Uses s-parameter behavioral models to represent the channel.• Evaluates signal integrity as a result of travelling from the transmitter to the receiver through the channel.

Veye 0‐pk

Heye %UI

Eye Diagram at Receiver outputEye Diagram at Receiver input

Machine Learning Projects Using HSSCDR

1. Determining worst case channel corner/design combination to determine whether channel will pass with tolerance effects considered.o Historically, this has been done by sweeping all possible

combinations. While accurate, this can take a lot of time and may lead to assumptions/pessimistic considerations in order to arrive at a solution within reasonable time.

o Machine learning models should be able make us arrive at accurate solutions in a much smaller time frame.

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Machine Learning Projects Using HSSCDR

2. Determining bath tub curve using 100K bits in stead of 10M bits. o Use of Kernel estimation method to learn the PDF of the received

signal at the output of the receiver. o The use of the obtained PDF to determine BER bathtub of channel.o Will lead to faster time to solution with at least similar accuracy.

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Machine Learning Projects Using HSSCDR

3. Developing a machine learned model for determining the eye, frequency response, BER tub of a particular channel topology given certain variable properties within the channel such as: lengths, connectors, materials, etc…o Extend this machine learned model using transfer learning to

determine properties of a particular channel topology (different from the aforementioned topology) given a certain frequency response, eye diagram and BER tub.

o Has great potential to reduce time to solution in addition to increasing flexibility on the different factors that could be considered to do an analysis with more coverage. Additionally, the extension can give us design constraints that would support required SI constraints (eye, frequency response, etc.)

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Imagine the Possibility

• Integrated development of methodologyo We want to work with CAEML to make the center a valuable

extension of our internal team to leverage the research in machine learning for our future methodology of high-speed serial link analysis.

• Not just serial link analysis: o The machine learning fundamentals developed are applied to a

broader set of important electronics design and analysis important to us.