Software-defined networking (SDN) architectures can significantly improve communications throughput and latency. This study developed a Q-learning based cognitive agent on the Intel Loihi for SDN applications. We utilize continuous learning to enable optimal route selection in continuously changing networking environments. The Loihi based agent was able to route packets to the most optimal path 90% of the time with continuously changing network link latency. Due to system power constraints, a simplified version of the agent was then developed for launch into space aboard a CubeSat. The CubeSat was launched in January 2022 and the applications developed ran successfully in space. This is the first launch of a neuromorphic system performing SDN operation into space.


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    Titel :

    Neuromorphic Hardware in Outer Space: Software Defined Networking Executed on an In-Orbit Loihi Spiking Processor


    Beteiligte:
    Rahman, Nayim (Autor:in) / Yakopcic, Chris (Autor:in) / Lent, Ricardo (Autor:in) / Briones, Janette C. (Autor:in) / Chelmins, David (Autor:in) / Dudukovitch, Rachel (Autor:in) / Smith, Aaron (Autor:in) / Gannon, Adam (Autor:in) / Lowry, Michael (Autor:in) / Murbach, Marcus S. (Autor:in)


    Erscheinungsdatum :

    20.06.2023


    Format / Umfang :

    1261833 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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