Exploring Neuromorphic Paradigms in Softwarized Networks: A Preliminary Study

Abstract

Nature has perfected efficient ways to process information-like how our brains quickly recognize faces or learn new skills using minimal energy. Neuromorphic computing is a biologically inspired computing paradigm that mimics the brain's neural architecture to achieve high efficiency and adaptive learning. Current research focuses on scaling neuromorphic systems for energy-efficient AI acceleration, robotics, video recognition, alongside advances in Spiking Neural Networks (SNNs), in-memory computing architectures (e.g., memristors), and hardware-software codesign for applications like autonomous systems and healthcare diagnostics. While neuromorphic computing has shown promise in other domains, its role in softwarized networks remains underexplored. This paper presents the early stage of the doctoral research, outlining the required investigations to leverage neuromorphic computing in softwarized networks. We discuss the methodology, involved challenges, limitations, and potential impacts of this novel intersection.