Professors Constantine Caramanis from WNCG (The Wireless Networking and Communications Group) and Michael Orshansky from CERC ICSG (Computer Engineering Research Center Integrated Circuits and Systems Group) have received a grant from the National Science Foundation to conduct interdisciplinary research into new techniques for robust automated design of analog integrated circuit design. The project, entitled Overcoming Nanoscale Modeling Challenges in Analog Synthesis: A Data-Driven Paradigm for Optimization of Approximate Functions, will develop a new approach for optimization over approximate descriptions of transistor behavior, which is the only realistic way to capture analog circuit behavior in a manner appropriate for automated synthesis. The approach develops techniques from machine learning and robust optimization, and is based on explicitly modeling the divergence between the exact model and the approximate model. The outcomes of the work under this proposal will lead to increased automation of analog and mixed-signal design, and result in higher design productivity, as well as more power-efficient and cheaper integrated circuits. Thus, this work will help sustain the evolution and growth of semiconductor technology. At the same time, the theoretical techniques and algorithms to be developed will also benefit other scientific domains in which optimization using approximate functions is used.
