Embedded Intelligence: State-of-the-Art and Research Challenges

Seng Kah Phooi; Li-Minn Ang

doi:10.1109/ACCESS.2022.3175574

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Embedded Intelligence: State-of-the-Art and Research Challenges

Journal article

Open access

Peer reviewed

Embedded Intelligence: State-of-the-Art and Research Challenges

Seng Kah Phooi and Li-Minn Ang

IEEE Access, Vol.10, pp.59236-59258

2022

DOI: https://doi.org/10.1109/ACCESS.2022.3175574

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Abstract

embedded systems

SoC

FPGA

GPU

parallel architecture

machine learning

deep learning

IoT

Edge AI

computer architecture

artificial intelligence

servers

field programmable gate arrays

classification algorithms

energy efficiency

cloud computing

Recent years have seen deployments of increasingly complex artificial intelligent (AI) and machine learning techniques being implemented on cloud server architectures and embedded into edge computing devices for supporting Internet of Things (IoT) and mobile applications. It is important to note that these embedded intelligence (EI) deployments on edge devices and cloud servers have significant differences in terms of objectives, models, platforms and research challenges. This paper presents a comprehensive survey on EI from four aspects: (1) First, the state-of-the-art for EI using a set of evaluation criteria is proposed and reviewed; (2) Second, EI for both cloud server accelerators and low-complexity edge devices are discussed; (3) Third, the various techniques for EI are categorized and discussed from the system, algorithm, architecture and technology levels; and (4) The paper concludes with the lessons learned and the future prospects are discussed in terms of the key role EI is likely to play in emerging technologies and applications such as Industry 4.0. This paper aims to give useful insights and future prospects for the developments in this area of study and highlight the challenges for practical deployments.

Details

Title: Embedded Intelligence: State-of-the-Art and Research Challenges
Authors: Seng Kah Phooi (Corresponding Author) - Xi’an Jiaotong-Liverpool University
Li-Minn Ang (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Publication details: IEEE Access, Vol.10, pp.59236-59258
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2022
DOI: 10.1109/ACCESS.2022.3175574
ISSN: 2169-3536
Copyright note: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 99640278002621
Output Type: Journal article

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Web Of Science research areas: Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications

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