Center For Advanced Electronics Through Machine Learning (CAEML)


Enable fast, accurate design and verification of microelectronic circuits and systems by creating machine learning algorithms to derive models used 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 while protecting intellectual property. A behavioral approach to systems modeling will meet these objectives. CAEML will pioneer the application of emerging machine-learning techniques to microelectronics and micro-systems modeling. Existing methods fall short when applied to systems with many ports, which contain reliability hazards, have non-linear responses, and have variability. This problem will be addressed jointly with our microelectronics industry partners whose diverse products include electronic design automation tools, integrated circuits, mobile systems, and test equipment. Close engagement with these industry partners will ensure that the Center provides models and tools that will facilitate communications between customers and suppliers across the entire industry value chain while protecting the proprietary information of all parties. This will lead to more efficient and reliable production, and better yields. CAEML will develop new domain-specific machine learning algorithms to extract models using limited training data. Designers’ prior knowledge is utilized to speed-up learning and to impose physical constraints on the models.

This is a NSF Industry/University Collaborative Research Center (I/UCRC) with University of Illinois, Urbana Champaign (lead) and North Carolina State University. The center is expected to start in Sep. 2016

CAEML Introduction



• Madhavan Swaminathan (Site Director)   • Chuanyi Ji


• Majid Ahadi   • Hakki Mert Torun  • Huan Yu

Industry Partners

• Analog Devices  • Cadence  • Cisco  • HPE  • IBM  • Lockheed Martin  • Nvidia  • Qualcomm  • Samsung  • Sandia National Labs  • Syposys  • Xilinx