Edge AI: A Comprehensive Survey of Technologies, Applications, and Challenges
V. S. Shankar Sriram
Abstract
The convergence of edge computing with Artificial Intelligence has accelerated growth of Edge AI, a transformative approach that brings intelligence straight to the point of data generation. Edge AI represents a transformative paradigm in artificial intelligence, facilitating information analysis and real-time decision making at the periphery of the network. This paper provides an overview of Edge AI, including its architectural models, key frameworks, and applications. Architectures such as Client-Server, Federated Learning, Pipeline, Distributed, and Hierarchical are explored, along with frameworks like PyTorch Mobile, ONNX, TensorFlow Lite, and OpenVINO, which facilitate the implementation of Artificial Intelligence models at the edge. Advantages of adopting Edge AI, such as scalability, bandwidth efficiency, resilience, enhanced security, and reduced latency are discussed, highlighting its suitability for various applications across industries. Applications of Edge AI in healthcare, smart homes, industrial IoT, autonomous vehicles, and retail are also examined. Overall, Edge AI represents a pragmatic methodology to unlock the full potential of Artificial Intelligence at the edge, paving way for a more intelligent and connected world.