Deployment Pipeline from Rockpool to Xylo for Edge Computing
December 15, 2024 ยท Declared Dead ยท ๐ arXiv.org
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Authors
Peng Zhou, Dylan R. Muir
arXiv ID
2412.11047
Category
cs.NE: Neural & Evolutionary
Cross-listed
cs.AI
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Deploying Spiking Neural Networks (SNNs) on the Xylo neuromorphic chip via the Rockpool framework represents a significant advancement in achieving ultra-low-power consumption and high computational efficiency for edge applications. This paper details a novel deployment pipeline, emphasizing the integration of Rockpool's capabilities with Xylo's architecture, and evaluates the system's performance in terms of energy efficiency and accuracy. The unique advantages of the Xylo chip, including its digital spiking architecture and event-driven processing model, are highlighted to demonstrate its suitability for real-time, power-sensitive applications.
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