Deploying AI on Edge: Advancement and Challenges in Edge Intelligence

Tianyu Wang, Jinyang Guo, Bowen Zhang, Ge Yang and Dong Li ()
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Tianyu Wang: State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Jinyang Guo: State Key Laboratory of Complex & Critical Software Environment, Beihang University, Beijing 100083, China
Bowen Zhang: Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Ge Yang: Institute of Artificial Intelligence, Beihang University, Beijing 100083, China
Dong Li: State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

Mathematics, 2025, vol. 13, issue 11, 1-21

Abstract: In recent years, artificial intelligence (AI) has achieved significant progress and remarkable advancements across various disciplines, including biology, computer science, and industry. However, the increasing complexity of AI network structures and the vast number of associated parameters impose substantial computational and storage demands, severely limiting the practical deployment of these models on resource-constrained edge devices. Although edge intelligence methods have been proposed to alleviate the computational and storage burdens, they still face multiple persistent challenges, such as large-scale model deployment, poor interpretability, privacy and security vulnerabilities, and energy efficiency constraints. This article systematically reviews the current advancements in edge intelligence technologies, highlights key enabling techniques including model sparsity, quantization, knowledge distillation, neural architecture search, and federated learning, and explores their applications in industrial, automotive, healthcare, and consumer domains. Furthermore, this paper presents a comparative analysis of these techniques, summarizes major trade-offs, and proposes decision frameworks to guide deployment strategies under different scenarios. Finally, it discusses future research directions to address the remaining technical bottlenecks and promote the practical and sustainable development of edge intelligence. Standing at the threshold of an exciting new era, we believe edge intelligence will play an increasingly critical role in transforming industries and enabling ubiquitous intelligent services.

Keywords: artificial intelligence; computational and storage demands; edge device deployment; edge intelligence; federated learning; industrial ai applications; knowledge distillation; model compression; network structure complexity; neural architecture search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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