Modular Machine Learning for Behavioral Modeling of Microelectronic Circuits and Systems

Modern machine learning algorithms are inherently modular. This modularity, combined with the behavioral approach to system design and simulation, will be leveraged to develop mathematical tools for assessing the performance and minimal data requirements for learning a low-complexity representation of the system behavior, one component or subsystem at a time.

Project PIs: Maxim Raginsky, Andreas Cangellaris

Project Research Thrust: Theory and Machine Learning Efficiency

Research Timeline Jan 1, 2017 – Dec 31, 2019