In recent years, innovation in analog-to-digital interfaces has been shaped by the data-driven nature of modern systems and their application needs. This has led to a new generation of analog-to-information interfaces, which blur the traditional building block boundaries and extract information through symbiotic interplay between analog and digital signal processing. In this talk, I will discuss the rationale behind this trend, starting from the general limitations of classical A/D converters and digital signal processing fabrics. Second, we look at specific research examples involving audio and image classification using analog feature extractors and compute elements in tiny machine learning (tinyML) systems. Third, we consider the emerging paradigm of end-to-end training and architecture search using differentiable circuit models. Overall, the resulting research direction calls for a holistic design space exploration that will require new ways of managing complexity and community-wide collaboration as typically seen in software projects. The final part of this presentation is therefore dedicated to the current movement toward open-source chip design and tooling, which will not enable new possibilities in research, but also fuel a new wave of democratization in microelectronics education.

Boris Murmann is a Professor of Electrical Engineering at Stanford University. He joined Stanford in 2004 after completing his Ph.D. degree in electrical engineering at the University of California, Berkeley in 2003. From 1994 to 1997, he was with Neutron Microelectronics, Germany, where he developed low-power and smart-power ASICs. Since 2004, he has worked as a consultant with numerous Silicon Valley companies. Dr. Murmann’s research interests are in mixed-signal integrated circuit design, including sensor interfaces, data conversion, high-speed communication, and embedded machine learning. He was a co-recipient of the Best Student Paper Award at the 2008 and 2021 VLSI Circuits Symposia, as well as a recipient of the Best Invited Paper Award at the 2008 IEEE Custom Integrated Circuits Conference (CICC). He received the 2009 Agilent Early Career Professor Award, the 2012 Friedrich Wilhelm Bessel Research Award by the Humboldt Foundation, and the 2021 SIA-SRC University Researcher Award for lifetime research contributions to the U.S. semiconductor industry. He has served as an Associate Editor of the IEEE Journal of Solid-State Circuits, an AdCom member and Distinguished Lecturer of the IEEE Solid-State Circuits Society (SSCS), the Data Converter Subcommittee Chair and Technical Program Chair of the IEEE International Solid-State Circuits Conference (ISSCC), as well as the Technical Program Co-Chair of the tinyML Research Symposium. He currently serves as the chair of the IEEE SSCS Technical Committee on Open-Source Ecosystem and the General Co-Chair of the 2023 IEEE International Symposium on Circuits and Systems (ISCAS). He is a Fellow of the IEEE.