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Review

Embedded Artificial Intelligence: A Comprehensive Literature Review

by
Xiaoyuan Huang
1,2,*,
Hongcheng Wang
2,
Shiyin Qin
2,3 and
Su-Kit Tang
1,*
1
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China
2
School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523808, China
3
School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
*
Authors to whom correspondence should be addressed.
Electronics 2025, 14(17), 3468; https://doi.org/10.3390/electronics14173468
Submission received: 23 July 2025 / Revised: 21 August 2025 / Accepted: 27 August 2025 / Published: 29 August 2025
(This article belongs to the Section Artificial Intelligence)
Versions Notes

Abstract

Embedded Artificial Intelligence (EAI) integrates AI technologies with resource-constrained embedded systems, overcoming the limitations of cloud AI in aspects such as latency and energy consumption, thereby empowering edge devices with autonomous decision-making and real-time intelligence. This review provides a comprehensive overview of this rapidly evolving field, systematically covering its definition, hardware platforms, software frameworks and tools, core algorithms (including lightweight models), and detailed deployment processes. It also discusses its widespread applications in key areas like autonomous driving and smart Internet of Things (IoT), as well as emerging directions. By analyzing its core challenges and innovative opportunities in algorithms, hardware, and frameworks, this review aims to provide relevant researchers and developers with a practical guidance framework, promoting technological innovation and adoption.
Keywords: EAI; deep learning; resource constraints; hardware platforms; software frameworks; model optimization; deployment EAI; deep learning; resource constraints; hardware platforms; software frameworks; model optimization; deployment

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MDPI and ACS Style

Huang, X.; Wang, H.; Qin, S.; Tang, S.-K. Embedded Artificial Intelligence: A Comprehensive Literature Review. Electronics 2025, 14, 3468. https://doi.org/10.3390/electronics14173468

AMA Style

Huang X, Wang H, Qin S, Tang S-K. Embedded Artificial Intelligence: A Comprehensive Literature Review. Electronics. 2025; 14(17):3468. https://doi.org/10.3390/electronics14173468

Chicago/Turabian Style

Huang, Xiaoyuan, Hongcheng Wang, Shiyin Qin, and Su-Kit Tang. 2025. "Embedded Artificial Intelligence: A Comprehensive Literature Review" Electronics 14, no. 17: 3468. https://doi.org/10.3390/electronics14173468

APA Style

Huang, X., Wang, H., Qin, S., & Tang, S.-K. (2025). Embedded Artificial Intelligence: A Comprehensive Literature Review. Electronics, 14(17), 3468. https://doi.org/10.3390/electronics14173468

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