In modern electronic systems, analog circuits are primarily used to condition and discretize physical signals, while most of the information processing occurs in the digital domain. The digital abstraction has allowed us to focus on bits as atomic carriers of information, largely ignore analog nuances in the underlying physical signals, and hence scale hierarchical systems toward transistor counts of over 100 billion on a single chip. While this approach has been a key enabler for managing complexity, it has always been scrutinized for its potential inefficiencies. Why don’t we prefer to perform an addition by simply connecting two wires carrying analog currents? Similarly, why not compute an integral by collecting charges on a capacitor? This talk will provide some answers to these questions, with a focus on AI/ML computing systems.

Biography:

Boris Murmann is a Professor of Electrical & Computer Engineering at the University of Hawaiʻi, Mānoa. From 2004-2023 he served as a Professor of Electrical Engineering at Stanford University. Since 2004, Dr. Murmann has worked as a consultant with numerous Silicon Valley companies. His research interests are in the area of mixed-signal integrated circuit design, including sensor interfaces, A/D and D/A conversion, high-speed communication links, embedded machine learning (tinyML), design methodology, as well as open-source chip design. He is a fellow of the IEEE and currently serves as the Editor-in-Chief of the IEEE Journal of Solid-State Circuits.