Edge AI and Real-Time Intelligent Computing

Abstract

ABSTRACT

Edge Artificial Intelligence (Edge AI) is emerging as a critical paradigm that enables real-time intelligent computing directly on edge devices such as sensors, smartphones, cameras, and embedded systems. Unlike traditional cloud-centric AI, Edge AI processes data locally near its source, reducing latency, bandwidth usage, and privacy risks. This paper presents a comprehensive review of Edge AI technologies, architectures, enabling hardware, and real-time computing frameworks. The study explores deployment strategies, optimization techniques, and application domains including smart cities, autonomous vehicles, healthcare monitoring, and industrial automation. Comparative analysis between cloud AI and edge AI approaches is also discussed. Furthermore, challenges such as energy constraints, model compression, security, and scalability are examined. The paper concludes with future research directions toward distributed intelligence and collaborative edge-cloud ecosystems. Edge AI is transforming intelligent computing by enabling faster decision-making and context-aware analytics in real-world environments.

KEYWORDS: Edge AI, Real-Time Computing, Embedded Intelligence, Edge Devices, Distributed AI, Low-Latency Processing, IoT Intelligence