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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.


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.

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