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A Low-Latency RDP-CORDIC Algorithm for Real-Time Signal Processing of Edge Computing Devices in Smart Grid Cyber-Physical Systems
Smart grids are being expanded in scale with the increasing complexity of the equipment. Edge computing is gradually replacing conventional cloud computing due to its low latency, low power consumption, and high reliability. The CORDIC algorithm has the characteristics of high-speed real-time proces...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571967/ https://www.ncbi.nlm.nih.gov/pubmed/36236587 http://dx.doi.org/10.3390/s22197489 |
Sumario: | Smart grids are being expanded in scale with the increasing complexity of the equipment. Edge computing is gradually replacing conventional cloud computing due to its low latency, low power consumption, and high reliability. The CORDIC algorithm has the characteristics of high-speed real-time processing and is very suitable for hardware accelerators in edge computing devices. The iterative calculation method of the CORDIC algorithm yet leads to problems such as complex structure and high consumption of hardware resource. In this paper, we propose an RDP-CORDIC algorithm which pre-computes all micro-rotation directions and transforms the conventional single-stage iterative structure into a three-stage and multi-stage combined iterative structure, thereby enabling it to solve the problems of the conventional CORDIC algorithm with many iterations and high consumption. An accuracy compensation algorithm for the direction prediction constant is also proposed to solve the problem of high ROM consumption in the high precision implementation of the RDP-CORDIC algorithm. The experimental results showed that the RDP-CORDIC algorithm had faster computation speed and lower resource consumption with higher guaranteed accuracy than other CORDIC algorithms. Therefore, the RDP-CORDIC algorithm proposed in this paper may effectively increase computation performance while reducing the power and resource consumption of edge computing devices in smart grid systems. |
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