A Review of Artificial Intelligence in Embedded Systems

• Published in: Micromachines (Basel) Vol. 14; no. 5; p. 897
• Main Authors: Zhang, Zhaoyun, Li, Jingpeng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22-04-2023; MDPI
• Subjects: Algorithms; Analysis; Artificial intelligence; Datasets; Design; Electronic devices; embedded intelligence; Embedded systems; Energy consumption; Energy efficiency; hardware accelerator; internet of things; model compression; Neural networks; resource-constrained device; Review; model compression; resource-constrained device; internet of things; embedded intelligence; hardware accelerator; artificial intelligence
• ISSN: 2072-666X; 2072-666X
• DOI: 10.3390/mi14050897

Advancements in artificial intelligence algorithms and models, along with embedded device support, have resulted in the issue of high energy consumption and poor compatibility when deploying artificial intelligence models and networks on embedded devices becoming solvable. In response to these problems, this paper introduces three aspects of methods and applications for deploying artificial intelligence technologies on embedded devices, including artificial intelligence algorithms and models on resource-constrained hardware, acceleration methods for embedded devices, neural network compression, and current application models of embedded AI. This paper compares relevant literature, highlights the strengths and weaknesses, and concludes with future directions for embedded AI and a summary of the article.