Title: Energy-efficient communication and computation for IoT:  fundamental limits, efficient strategies, and application to biosensing wearables

Abstract: How do we minimize energy required in short-distance communications? What is the minimum energy required to compute reliably using error and delay-prone gates or processors? With the advent of IoT and saturation of Moore's law (and Dennard's scaling), these questions have becoming increasingly important as researchers seek technologies for high-speed low-energy communications, and efficient alternatives to ultra-reliable CMOS devices. I’ll talk about our work on both fundamental limits on energy requirements (and how Shannon theory changes when computation is brought in), as well as novel strategies and architectures for minimizing communication and computation energy. This includes new coding techniques as well as strategies that perform reliable machine-learning on error/delay-prone and energy-limited components and sensors. Finally, I’ll talk about application of these ideas in design and implementation of IoT for noninvasive biopotential measurement, e.g. for neural interfaces. I'll discuss how a novel “hierarchical” architecture that limits error-accumulation turns out to have a substantially improved information-energy dissipation tradeoff than simply “compressing innovations” (a strategy known to be suboptimal from a work of Kim and Berger). This is a part of a larger work on utilizing information theory to motivate and engineer ultra-high-density neural sensing interfaces, as well as provide fundamental limits on their precision and performance.

Bio: Pulkit Grover (Ph.D. UC Berkeley'10, B.Tech.'03, M.Tech.'05 IIT Kanpur) is an assistant professor at CMU (2013-), working on information theory, circuit design, and biomedical engineering. His main contributions to science are towards developing a new theory of information (fundamental limits and practical designs) for low-energy communication, sensing, and computing by incorporating novel (noisy and noiseless) circuit-energy models to add to classical communication or sensing energy models. To apply these ideas to a variety of problems including communication, computing, sensing, and novel biomedical systems, his lab works extensively with circuit engineers, neuroscientists, and doctors. Pulkit is the recipient of the 2010 best student paper award at the IEEE Conference in Decision and Control (CDC); a 2010 best student paper finalist at the IEEE International Symposium on Information Theory (ISIT); the 2011 Eli Jury Dissertation Award from UC Berkeley; the 2012 Leonard G. Abraham best journal paper award from the IEEE Communications Society; a 2014 best paper award at the International Symposium on Integrated Circuits (ISIC); a 2014 NSF CAREER award; and a 2015 Google Research Award.