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Published VersionCC BY V4.0, Open Access
Journal article
Embedded Intelligence on FPGA: Survey, Applications and Challenges
Electronics, Vol.10(8), pp.1-34
2021
Abstract
Embedded intelligence (EI) is an emerging research field and has the objective to incorporate machine learning algorithms and intelligent decision-making capabilities into mobile and embedded devices or systems. There are several challenges to be addressed to realize efficient EI implementations in hardware such as the need for: (1) high computational processing; (2) low power consumption (or high energy efficiency); and (3) scalability to accommodate different network sizes and topologies. In recent years, an emerging hardware technology which has demonstrated strong potential and capabilities for EI implementations is the FPGA (field programmable gate array) technology. This paper presents an overview and review of embedded intelligence on FPGA with a focus on applications, platforms and challenges. There are four main classification and thematic descriptors which are reviewed and discussed in this paper for EI: (1) EI techniques including machine learning and neural networks, deep learning, expert systems, fuzzy intelligence, swarm intelligence, self-organizing map (SOM) and extreme learning; (2) applications for EI including object detection and recognition, indoor localization and surveillance monitoring, and other EI applications; (3) hardware and platforms for EI; and (4) challenges for EI. The paper aims to introduce interested researchers to this area and motivate the development of practical FPGA solutions for EI deployment.
Details
- Title
- Embedded Intelligence on FPGA: Survey, Applications and Challenges
- Authors
- Jasmine Kah Phooi Seng (Author) - UNSW AustraliaPaik Jen Lee (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringKenneth Ang (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Electronics, Vol.10(8), pp.1-34
- Publisher
- MDPI AG
- Date published
- 2021
- DOI
- 10.3390/electronics10080895
- ISSN
- 2079-9292
- Copyright note
- Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99522305402621
- Output Type
- Journal article
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- Computer Science, Information Systems
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- Physics, Applied
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