Wearable electronics, intelligent devices, medical electronics, and more recently Internet of Things (IoTs) are dramatically changing the way we experience life by providing rich information about our activities, health, and the environment. To be truly ubiquitous, these devices must be energy autonomous. Such a system must harvest energy from ambient sources like light, vibration, temperature differentials, etc. and must also be very efficient in using the little energy available to it for computation. This talk focuses on how to enable perpetual, self-sustaining ultra-low power systems. We will discuss circuits that can harvest energy efficiently from the smallest ambient sources, and ultra-low power analog and mixed signal circuits such as voltage references, clock generators and regulators. We will also talk about the need for an ultra-low power system architecture where energy takes the center stage in defining the architecture and not performance as in traditional systems. Finally, we will discuss the use of ultra-low power systems for the development of bio-electronics and neurological systems, and power-electronics with an emphasis on the use of renewable energy.
Aatmesh Shrivastava received the Ph.D. degree in Electrical Engineering from University of Virginia in 2014. Prior to his Ph.D., he worked as a senior design engineer at Texas Instruments, Bangalore from 2006 to 2010. From 2014 to 2016 he worked at an IoT start-up PsiKick, where he headed the research and development of the energy harvesting and power management solutions. He is currently working as an Assistant Professor in the Electrical Engineering department at Northeastern University, Boston where he is leading the Energy Circuit Group. He has more than 20 patents and has published more than 25 peer reviewed papers in top IEEE conferences and journals. His research interests include self-powered and ultra-low power circuits and system, energy-harvesting and power-first system/computer architecture, internet-of-things, ultra-low power bio-medical and neural Circuits, exascale computing, and high reliability system design.