Machine Learning for High-Speed Channel Design. Dale Becker and Jose Hejase IBM Systems

Transcription

1 Machine Learning for High-Speed Channel Design Dale Becker and Jose Hejase IBM Systems

2 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

3 Genetic Algorithms Applied to SI EPEPS o I/O Buffers ca to improve worst case corner prediction and simulation times o Bit string analysis of source synchronous eye diagrams IBM Application o Internal IBM code - SNAP o Optimization of channel parameters given a set of package models in HSSCDR and maximization or minimization of channel response o HSSCDR = IBM s high speed serial simulator A Desire o Develop machine learning techniques to advance the power of our high-speed serial analysis o Leverage the machine learning fundamentals to a broad set of our design and analysis applications

4 Frequency-Domain Compliance Method - Motivation Our initial foray into Machine Learning o 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. Winet al., "High speed bus signal integrity compliance using a frequency domain model," 2016 IEEE International Symposium on Electromagnetic Compatibility (EMC), Ottawa, ON, 2016, pp 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

5 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 ILDA SXTF SXTB SXTA

6 Frequency-Domain Compliance Method Model Development Run a large amount of full time-domain simulations on channels with variable properties o Full HSSCDR runs including Tx/Rx IP and equalization presets Procedure for finding the frequency domain model

7 Frequency-Domain Compliance Method Application to Channel

8 Frequency-Domain Compliance Method Benefits Reduces simulation time with less experienced users. o 10 seconds vs. multiple 15 minutes10 7 bit simulation o 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 Gb/s Frequency domain compliance method is bus topology independent o Note: As more simulations are run, learned method predicts pass / fail with less margin

9 Machine Learning Projects Using HSSCDR Ongoing work with Georgia Tech as part of CAEML

10 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. Eye Diagram at Receiver input Eye Diagram at Receiver output Veye 0 pk Heye %UI

11 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. 11

12 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. 12

13 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.) 13

14 Imagine the Possibility Integrated development of methodology o 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.