EDGE AI: Intelligent Computing At The Edge for Real-Time Applications — A Review of Architectures, Model Optimization and Deployment

Abstract

Edge Artificial Intelligence (Edge AI) combines machine learning with edge computing so that data are processed on or near the devices that produce them, such as sensors, cameras, smartphones, IoT nodes and embedded systems, rather than entirely in remote cloud servers. This brings decisions closer to the data source, reducing latency, bandwidth use and dependence on connectivity while improving privacy. This paper reviews the foundations and practice of Edge AI for real-time applications. It examines the edge computing paradigm and edge intelligence levels, the partitioning of neural networks between device, edge server and cloud, efficient architectures such as depthwise-separable networks, early-exit inference, and compression by pruning, quantization and knowledge distillation. Tiny machine learning on microcontrollers and standard benchmarks for measuring edge inference are discussed. Applications in smart surveillance, industrial monitoring, healthcare wearables, autonomous robots and smart cities are summarized. A layered device–edge–cloud architecture is proposed, together with research gaps, objectives, a comparison of deployment tiers and an analysis of challenges including limited resources, heterogeneity, security and model updates. The review concludes that Edge AI is essential for low-latency, private and reliable intelligent systems.