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一种用于智能电网信息物理系统中边缘计算设备实时信号处理的低延迟RDP-CORDIC算法。

A Low-Latency RDP-CORDIC Algorithm for Real-Time Signal Processing of Edge Computing Devices in Smart Grid Cyber-Physical Systems.

作者信息

Qin Mingwei, Liu Tong, Hou Baolin, Gao Yongxiang, Yao Yuancheng, Sun Haifeng

机构信息

School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China.

Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621000, China.

出版信息

Sensors (Basel). 2022 Oct 2;22(19):7489. doi: 10.3390/s22197489.

DOI:10.3390/s22197489
PMID:36236587
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9571967/
Abstract

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.

摘要

随着设备复杂性的增加,智能电网的规模正在不断扩大。边缘计算因其低延迟、低功耗和高可靠性,正逐渐取代传统云计算。CORDIC算法具有高速实时处理的特点,非常适合边缘计算设备中的硬件加速器。然而,CORDIC算法的迭代计算方法会导致结构复杂和硬件资源消耗高等问题。在本文中,我们提出了一种RDP-CORDIC算法,该算法预先计算所有微旋转方向,并将传统的单级迭代结构转换为三级和多级组合迭代结构,从而使其能够解决传统CORDIC算法迭代次数多、消耗大的问题。还提出了一种方向预测常数的精度补偿算法,以解决RDP-CORDIC算法高精度实现中ROM消耗大的问题。实验结果表明,与其他CORDIC算法相比,RDP-CORDIC算法具有更快的计算速度、更低的资源消耗和更高的精度保证。因此,本文提出的RDP-CORDIC算法可以在提高计算性能的同时,降低智能电网系统中边缘计算设备的功耗和资源消耗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/db80ede6985b/sensors-22-07489-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/4dfde41eb0b3/sensors-22-07489-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/cac5258e28f1/sensors-22-07489-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/618b6df3bd82/sensors-22-07489-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/702cdcb62891/sensors-22-07489-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/09798c695230/sensors-22-07489-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/a5f71e087c8b/sensors-22-07489-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/53d8b8c657c9/sensors-22-07489-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/989093e11ab7/sensors-22-07489-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/d934cff4e21b/sensors-22-07489-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/db80ede6985b/sensors-22-07489-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/4dfde41eb0b3/sensors-22-07489-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/cac5258e28f1/sensors-22-07489-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/618b6df3bd82/sensors-22-07489-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/702cdcb62891/sensors-22-07489-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/09798c695230/sensors-22-07489-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/a5f71e087c8b/sensors-22-07489-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/53d8b8c657c9/sensors-22-07489-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/989093e11ab7/sensors-22-07489-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/d934cff4e21b/sensors-22-07489-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f18/9571967/db80ede6985b/sensors-22-07489-g010.jpg

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