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This decade has witnessed wide use of data-driven systems, from multimedia to scientific computing, and in each case quality data movement infrastructure is required, many with SerDes as a cornerstone. On the one hand, HPC and machine learning cloud infrastructure carry exabytes of data in a year through the backplanes of data centers. On the other hand, the growing need for edge computing in the IoT places a tight envelope on the energy per bits. In this survey, we give a system level overview of the common design challenges in implementing SerDes solutions under different scenarios and propose simulation methods benefiting from advanced machine learning techniques. Preliminary results with the proposed simulation platform are demonstrated and analyzed through machine learning based design methodologies.

System Level Optimization for High-Speed SerDes: Background and the Road Towards Machine Learning Assisted Design Frameworks