Research on high-speed fuzzy reasoning with CPLD for fault diagnosis expert system

Xue, Jiang; Sun, Laijun; Qiao, Changming; Qian, Haibo

doi:10.1109/icemi.2009.5274648

Cited by 1 publication

(3 citation statements)

References 5 publications

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“…Machine Learning and Neural Network [5][6][7][8][9][10][11][12][13][14][15][16][17] Deep Learning [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] Expert System [36][37][38][39] Fuzzy Intelligence [40][41][42][43][44][45][46] Swarm Intelligence [47][48][49][50][51] SOM and Extreme Learning [52][53][54][55]…”

Section: Ei Techniquesmentioning

confidence: 99%

“…The proposed method shows that hardware implementation is able to achieve speeds of at least 6x faster than software implementation. The high level of the expert system and the realizing process of the expert system with FPGA are discussed in [37].…”

Section: Expert Systemmentioning

confidence: 99%

“…Furthermore, there are two main types of interface, which are known as the serial interface and parallel interface. Serial interface design shows that this approach is seven times slower in the communication speed as compared to a parallel interface, but fewer ports are occupied, whereas the parallel interface occupies many system ports but its read-write speed is very high [37]. There is another piece of research done by [38] on expert systems based on expert reasoning rules and flow charts of expert adaptive control.…”

Section: Expert Systemmentioning

confidence: 99%

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Embedded Intelligence on FPGA: Survey, Applications and Challenges

2021

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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.

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