Neuromorphic Edge AI System

Abstract

The rapid expansion of sensor networks in applications such as environmental monitoring, healthcare, and smart infrastructure has raised significant concerns regarding energy efficiency and real-time processing at the edge. Traditional AI approaches often depend on cloud computing, which introduces latency, security risks, and high energy demand. Neuromorphic computing offers a transformative alternative by emulating the brain’s biological processes to achieve low-power, adaptive, and parallel computation. By integrating spiking neural networks (SNNs) into edge devices, neuromorphic systems provide intelligent decision-making closer to the data source, reducing communication overhead and enabling sustainability. This paper explores how brain-inspired neuromorphic architectures can enhance the scalability, energy efficiency, and autonomy of sensor networks, positioning them as a viable solution for next-generation Edge AI