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Said Hamdioui

Professor

    Delft University of Technology, Netherlands

    Said Hamdioui is Chair Professor of Dependable and Emerging Computer Technologies at Delft University of Technology (TU Delft), where he leads the Computer Engineering Laboratory and previously headed the Quantum and Computer Engineering Department (2019-2023). As co-founder/CEO of Cognitive-IC, he bridges academia and industry in hardware dependability solutions. His pioneering research spans emerging computing paradigms (memristors, in-memory computing, brain-inspired architectures) and hardware dependability (testing, reliability, security). An Intel and Philips/NXP Semiconductors veteran, Prof. Hamdioui brings 25+ years of industry-academic expertise. He holds four patents, has authored 330+ publications, and consulted for leading semiconductor firms including Intel, NXP, and Huawei. His innovations in
    computation-in-memory architectures are implemented in European Space Agency missions and industrial AI accelerators. His work on memory testing techniques earned the EDAA Outstanding Dissertation Award (2001), with subsequent honors including the HiPEAC Technology Transfer Award (2015, 2022) and European Commission Innovation Award (2020) for the MNEMOSENE project. An IEEE Distinguished Lecturer (2021-2022) and Netherlands Academy of Engineering Fellow, he serves on editorial boards for ACM JETC and IEEE Design & Test. His 20+ Best Paper Awards and 2017 Teacher of the Year recognition at TU Delft underscore dual excellence in research and education. Recently elected to the Royal Holland Society of Sciences (2023), he also contributes to EU policy on semiconductor sovereignty. Currently advising Khalifa University (UAE) and Stuttgart’s Graduate School on Test/Reliability, he ranks among the world’s top 2% scientists (Stanford 2024). His current projects focus on quantum-classical computing interfaces and AI hardware security for critical infrastructure.

    Talk Title:Hardware-Enabled Trust: Secure In-Memory Computing Architectures for Resilient AI Systems

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