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DesignCon 2019 Presentation Viewer

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.

Machine Learning Methods in High-Speed Channel Modeling

Tianjian Lu (Hardware Engineer, Google)

Ken Wu (Hardware Engineer, Google)

Location: Ballroom A

Date: Thursday, January 31

Time: 2:00pm - 2:40pm

Track: 15. Machine Learning for Microelectronics, Signaling & System Design, 01. Signal & Power Integrity for Single-Multi Die, Interposer & Packaging

Session Type: Technical Session

Vault Recording: TBD

Audience Level: All

The simulation techniques involved in high-speed channel simulations, which includes electromagnetic solvers in extracting interconnect models and circuit simulations in generating transient waveforms, can be very computationally expensive. There are efforts in developing novel numerical schemes in enhancing the computation efficiency. In this work, we propose improving the computational efficiency by taking advantage of existing data and machine learning techniques. On one hand, we make predictions on the circuit-level transient behaviors with recurrent neural networks; on the other hand, we predict eye-diagram metrics through solving a regression problem with support vector machines and neural networks.

Takeaway

Applying machine learning methods in high-speed channel modeling requires no complex circuit simulations or substantial domain knowledge. The learning-based model can be achieved within a reasonable amount of time on modern computing hardware. Once the training concludes, predictions can be make in a highly efficiency manner.