Affiliation:
1. School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523000, China
2. School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523000, China
Abstract
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.
Funder
Department of Education of Guangdong Province
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering
Cited by
9 articles.
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