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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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
26 March 2024
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| In: |
Frontiers in neuroscience
Year: 2024, Volume: 18, Pages: 1-6 |
| ISSN: | 1662-453X |
| DOI: | 10.3389/fnins.2024.1360122 |
| Online Access: | 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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| Author Notes: | Yannik Stradmann and Johannes Schemmel |
| Summary: | <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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| Item Description: | Gesehen am 27.05.2024 |
| Physical Description: | Online Resource |
| ISSN: | 1662-453X |
| DOI: | 10.3389/fnins.2024.1360122 |