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Marian Verhelst
Enabling AI at the Extreme Edge
Abstract:
Various applications demand more and more powerful machine inference in resource-scarce distributed devices. To allow intelligent applications at ultra-low energy and low latency, one needs 1.) custom AI processors, exploiting parallelism and data reuse under strong resource limitations; 2.) efficient ML models, optimized for the target hardware platform; 3.) data-efficient scheduling techniques and algorithm-to-hardware mapping tools. This talk will zoom into such a future of cross-layer optimized AI platforms for edge computing.
Bio:
Marian Verhelst is a professor at the MICAS lab of KU Leuven and a research director at imec. Her research focuses on embedded machine learning, hardware accelerators, and low-power edge processing. She received a PhD from KU Leuven in 2008, and worked as a research scientist at Intel Labs from 2008 till 2010. Marian is a scientific advisor to multiple startups, member of the board of ECSA, and served in the board of directors of tinyML. She is a science communication enthusiast as an IEEE SSCS Distinguished Lecturer, as a regular member of the Nerdland science podcast (in Dutch), and as the founding mother of KU Leuven’s InnovationLab high school program. Marian acquired 2 ERC grants and received the laureate prize of the Royal Academy of Belgium in 2016, the 2021 Intel Outstanding Researcher Award, and the André Mischke YAE Prize for Science and Policy in 2021.
Prabal Dutta
Expeditions in Low-Power Sensing: Past, Present, and Future
Abstract:
Today, much of the original vision of wireless sensors had been realized, and a bewildering array and variety of systems have been fielded that allow us to gather and process unprecedented amounts and types of data about the physical world. But this progress has also exposed many new challenges and opportunities. This talk will draw on my lab’s efforts in designing, deploying, and commercializing wireless sensors for a range of applications. The evolution of these efforts—from seemingly trivial connected sensors with simple cloud analytics to more complex networked sensors with sophisticated sensing and communications to sustainable perceptual networks that perform multi-spectral data fusion and inference at the edge to detect complex but sparse faults—has highlighted numerous exciting technical and methodological challenges ripe for attention.
Bio:
Prabal Dutta is a Professor of Electrical Engineering and Computer Sciences at University of California, Berkeley. His interests span circuits, systems, and software, with a focus on mobile, wireless, embedded, networked, and sensing systems that have applications in health, energy, and the environment. His work has yielded dozens of hardware and software systems, has won a Test-of-Time Award (SenSys’22), five Top Pick/Best Paper Awards (MICRO’16, SenSys'10, IPSN'10, HotEmNets'10, and IPSN'08), two Best Paper Nominees, numerous demo, design, poster, and industry contests, has been directly commercialized by a dozen companies and indirectly by many dozens more, and is on display at Silicon Valley’s Computer History Museum. His work has been recognized with an Okawa Foundation Grant, a Sloan Fellowship, an NSF CAREER Award, a Popular Science Brilliant Ten Award, and an Intel Early Career Award. He has served as a program chair for MobiSys, BuildSys, SenSys, IPSN, HotMobile, ESWEEK IoT Day, HotMobile, and HotPower, as general chair for EWSN, and as a member of the DARPA ISAT and DSSG study groups. He holds a Ph.D. in Computer Science from UC Berkeley, and an MS in Electrical Engineering and a BS in Electrical & Computer Engineering from The Ohio State University. He has co-founded several companies based on his research including Cubeworks, Gridware, nLine, and Vizi.
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