A memristor based neuromorphic processor for on-chip training is presented. Additionally, a novel approach utilizing in-situ learning to improve wireless signal modulation classification under adversarial jamming is described. The neuromorphic system is over 50× energy efficient than optimized digital systems at this wireless signal modulation task for similar accuracy levels.


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    Titel :

    Memristor Based Online Learning Neuromorphic Processor for Adaptive Modulation Spectrum Sensing in Communication Jammed Environments


    Beteiligte:
    Alam, Md. Shahanur (Autor:in) / Zhang, Shuo (Autor:in) / Yakopcic, Chris (Autor:in) / Taha, Tarek (Autor:in)


    Erscheinungsdatum :

    28.08.2023


    Format / Umfang :

    1265920 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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