SPCL_Bcast Virginia Smith The Scalable Parallel Computing Lab's *SPCL_Bcast* seminar continues with *Marian Verhelst of KU Leuven* presenting on *Heterogeneous multi-core systems for efficient EdgeML*. Everyone is welcome to attend (over Zoom)! When: Thursday, 26th October, 9AM CET Where: Zoom Join <https://spcl.inf.ethz.ch/Bcast/join> Abstract: Embedded ML applications are characterized by increasingly diverse workloads, forming a rich mixture of signal processing, GeMM and conv kernels, attention layers, and even graph processing. Accelerator efficiency suffers from supporting this wide variety of kernels. Heterogeneous multicore systems can offer a solution but come with their own challenges, such as: 1.) How to find the most optimal combination of cores?; 2.) How to efficiently map workloads across cores?; 3.) How to share data between these cores? This talk will report on a heterogeneous multi-core system for embedded neural network processing taped out at KULeuven MICAS. Moreover, it will give an outlook on work in progress towards further expanding this system for covering more workloads and more heterogeneous cores. Biography: Marian Verhelst is a full professor at the MICAS laboratories of KU Leuven and a research director at imec. Her research focuses on embedded machine learning, hardware accelerators, HW-algorithm co-design and low-power edge processing. She received a PhD from KU Leuven in 2008, and worked as a research scientist at Intel Labs, Hillsboro OR from 2008 till 2010. Marian is a member of the board of directors of tinyML and active in the TPC’s of DATE, ISSCC, VLSI and ESSCIRC and was the chair of tinyML2021 and TPC co-chair of AICAS2020. Marian is an IEEE SSCS Distinguished Lecturer, was a member of the Young Academy of Belgium, an associate editor for TVLSI, TCAS-II and JSSC and a member of the STEM advisory committee to the Flemish Government. Marian received the laureate prize of the Royal Academy of Belgium in 2016, the 2021 Intel Outstanding Researcher Award, the André Mischke YAE Prize for Science and Policy in 2021, and two ERC grants. More details & future talks <https://spcl.inf.ethz.ch/Bcast/> Scalable Parallel Computing Lab (SPCL) Department of Computer Science, ETH Zurich Website <https://spcl.inf.ethz.ch> X(Twitter) <https://twitter.com/spcl_eth> YouTube <https://www.youtube.com/@spcl> GitHub <https://github.com/spcl>