PUSHING INTELLIGENCE TO THE EDGE: EMBEDDED AI FOR SUSTAINABLE AND ADAPTIVE ELECTRONICS

Abstract

The rapid evolution of edge computing and artificial intelligence (AI) is reshaping how electronic systems are designed and deployed. This paper explores the paradigm of “Pushing Intelligence to the Edge”—embedding AI directly into smart sensors, ultra-low-power chips, and adaptive control modules to create sustainable and energy-efficient electronic systems. Traditional cloud-based AI architectures suffer from high latency, bandwidth constraints, and increased energy consumption. In contrast, edge AI enables realtime decision-making, data privacy, and reduced power requirements by bringing computational intelligence closer to the data source. The paper examines advanced hardware–software codesign techniques, lightweight neural network architectures, and neuromorphic processors as enablers of adaptive edge devices. Additionally, it addresses sustainability by investigating lowpower design strategies, green semiconductor materials, and dynamic power management. Through a comprehensive review and proposed design framework, this research highlights how embedded AI at the edge can deliver scalable, secure, and adaptive electronics for nextgeneration applications ranging from environmental monitoring to autonomous vehicles