Machine Learning for Electronic Design Automation

Electronic design automation must evolve in response to increasingly ambitious goals for low power and high performance, which are accompanied by a decreasing design cycle time. There is an unmet need for models, methods and tools that enable fast and accurate design and verification of microelectronic circuits and systems while protecting intellectual property. A behavioral approach to systems modeling will help achieve those objectives. Designers’ prior knowledge may be used to impose physical constraints on the models and to speed up learning.

This presentation will introduce the Center for Advanced Electronics through Machine Learning (CAEML). CAEML is an NSF-sponsored Industry/University Cooperative Research Center whose mission is to advance the state-of-the-art in electronic design automation (EDA) by using machine learning methods and algorithms. CAEML researchers are located at the University of Illinois at Urbana-Champaign, Georgia Tech, and North Carolina State University.

CAEML’s initial research efforts were primarily in the realms of behavioral modeling and system optimization, but have recently expanded to encompass data analytics and deep learning. Applications under investigation range from IP reuse to signal integrity analysis, and from FPGA compilation recipes to system ESD design. Many of the CAEML researchers use MATLAB tools in their work, including the toolboxes for system identification, global optimization, machine learning, and neural networks.

This talk will provide a brief overview of the center’s research portfolio and will include in-depth examples of the work being done to advance IP reuse, thermal optimization of 3D-IC, and system ESD design.