Deployment Pipeline from Rockpool to Xylo for Edge Computing

TL;DR

This paper presents a novel deployment pipeline integrating Rockpool with the Xylo neuromorphic chip, enabling efficient deployment of Spiking Neural Networks for ultra-low-power edge computing applications.

Contribution

It introduces a new deployment pipeline that combines Rockpool and Xylo, optimizing SNN deployment for edge devices with enhanced energy efficiency and real-time processing.

Findings

Achieved significant energy savings in SNN deployment.

Demonstrated high accuracy on edge computing tasks.

Validated system performance on Xylo hardware.

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.