Archimedes Postdoc Vassilis Alimisis Will Conduct a Tutorial at ICM 2025
Vassilis Alimisis, a Postdoc at the Archimedes Unit of the Athena Research Center in Greece, a Collaborating Researcher at the School of Electrical and Computer Engineering at the National Technical University of Athens in Greece, an Adjunct Lecturer at the Department of Digital Industry Technologies at the National and Kapodistrian University of Athens in Greece, and a National Postdoctoral Research Fellow of the Bodossaki Foundation, Athens, Greece, will deliver a tutorial at the 2025 International Conference on Microelectronics (ICM), which will take place on 14-17 December 2025, in Cairo, Egypt.
The International Conference on Microelectronics (ICM) is a high-level conference which brings together academics, researchers, engineers, and industry professionals and focuses on the latest developments in the field of circuits and systems and is organized by the IEEE Circuits and Systems Society (CASS). The theme of the ICM 2025 event is "Innovations in Circuits and Systems for a Sustainable Future" and comprises nine sub-themes: Artificial Intelligence and Deep Learning, Education in Circuits and Systems, Internet of Things (IoT), Intelligent Video Analytics and Vision Systems, Store Class Memories and Computational Storage, Brain: Innovative NeuroTechnologies, Green and Sustainable Computing and Systems, Analog and Mixed Signal Circuits and Systemsing, and Power and Energy Circuits and Systems.
Vassilis' tutorial is about "Low-Power Analog Hardware Classifier Architecture for ML Applications: From A to Z" and provides "a comprehensive introduction to the design and implementation of low power analog hardware classifiers tailored for machine learning (ML) applications, with a focus on energy-constrained environments such as edge devices and biomedical wearables." This tutorial will focus on hands-on case studies and provide important theoretical insights as well as practical knowledge. The tutorial will be particularly useful to engineers, researchers, and graduate students with an active interest in analog/mixed-signal IC design, edge ML systems, and energy-efficient hardware.
