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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.
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