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Closing the loop: high-speed robotics with accelerated neuromorphic hardware

<p>The BrainScaleS-2 system is an established analog neuromorphic platform with versatile applications in the diverse fields of computational neuroscience and spike-based machine learning. In this work, we extend the system with a configurable realtime event interface that enables a tight coup...

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Hauptverfasser: Stradmann, Yannik (VerfasserIn) , Schemmel, Johannes (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 26 March 2024
In: Frontiers in neuroscience
Year: 2024, Jahrgang: 18, Pages: 1-6
ISSN:1662-453X
DOI:10.3389/fnins.2024.1360122
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.3389/fnins.2024.1360122
Verlag, kostenfrei, Volltext: https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1360122/full
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Verfasserangaben:Yannik Stradmann and Johannes Schemmel

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520 |a <p>The BrainScaleS-2 system is an established analog neuromorphic platform with versatile applications in the diverse fields of computational neuroscience and spike-based machine learning. In this work, we extend the system with a configurable realtime event interface that enables a tight coupling of its distinct analog network core to external sensors and actuators. The 1,000-fold acceleration of the emulated nerve cells allows us to target high-speed robotic applications that require precise timing on a microsecond scale. As a showcase, we present a closed-loop setup for commuting brushless DC motors: we utilize PyTorch to train a spiking neural network emulated on the analog substrate to control an electric motor from a sensory event stream. The presented system enables research in the area of event-driven controllers for high-speed robotics, including self-supervised and biologically inspired online learning for such applications.</p> 
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