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Video s3
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    Presenter(s)
    Paolo Gibertini Headshot
    Display Name
    Paolo Gibertini
    Affiliation
    Affiliation
    NaMLab gGmbH
    Country
    Author(s)
    Display Name
    Paolo Gibertini
    Affiliation
    Affiliation
    NaMLab gGmbH
    Display Name
    Luca Fehlings
    Affiliation
    Affiliation
    NaMLab gGmbH
    Display Name
    Suzanne Lancaster
    Affiliation
    Affiliation
    NaMLab gGmbH
    Display Name
    Quang Duong
    Affiliation
    Affiliation
    NaMLab gGmbH
    Display Name
    Thomas Mikolajick
    Affiliation
    Affiliation
    NaMLab gGmbH
    Affiliation
    Affiliation
    Helmholtz Zentrum Berlin, Free University Berlin
    Display Name
    Stefan Slesazeck
    Affiliation
    Affiliation
    NaMLab gGmbH
    Display Name
    Erika Covi
    Affiliation
    Affiliation
    NaMLab, Dresden, Germany
    Display Name
    Veeresh Deshpande
    Affiliation
    Affiliation
    Helmholtz Zentrum Berlin
    Abstract

    Event-based neuromorphic systems provide a low-power solution by using artificial neurons and synapses to process data asynchronously in the form of spikes. Ferroelectric Tunnel Junctions (FTJs) are ultra low-power memristive devices and are well-suited to be integrated in these systems. Here, we present a hybrid FTJ-CMOS Integrate-and-Fire neuron which constitutes a fundamental building block for new-generation neuromorphic networks for edge computing.

    Slides
    • A Ferroelectric Tunnel Junction-Based Integrate-and-Fire Neuron (application/pdf)