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Article

Accelerating Faceting Wide-Field Imaging Algorithm with FPGA for SKA Radio Telescope as a Vast Sensor Array

1
School of Microelectronics, Shanghai Jiao Tong University, Shanghai 200240, China
2
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
This work is an extended version of paper entitled “An FPGA-Based Hardware Acceleration for Key Steps of Facet Imaging Algorithm” published in 2019 IEEE International Conference on Smart Cloud (SmartCloud).
Sensors 2020, 20(15), 4070; https://doi.org/10.3390/s20154070
Received: 5 April 2020 / Revised: 8 July 2020 / Accepted: 14 July 2020 / Published: 22 July 2020
(This article belongs to the Special Issue Smart Cloud Computing Technologies and Applications)
The SKA (Square Kilometer Array) radio telescope will become the most sensitive telescope by correlating a huge number of antenna nodes to form a vast array of sensors in a region over one hundred kilometers. Faceting, the wide-field imaging algorithm, is a novel approach towards solving image construction from sensing data where earth surface curves cannot be ignored. However, the traditional processor of cloud computing, even if the most sophisticated supercomputer is used, cannot meet the extremely high computation performance requirement. In this paper, we propose the design and implementation of high-efficiency FPGA (Field Programmable Gate Array) -based hardware acceleration of the key algorithm, faceting in SKA by focusing on phase rotation and gridding, which are the most time-consuming phases in the faceting algorithm. Through the analysis of algorithm behavior and bottleneck, we design and optimize the memory architecture and computing logic of the FPGA-based accelerator. The simulation and tests on FPGA are done to confirm the acceleration result of our design and it is shown that the acceleration performance we achieved on phase rotation is 20× the result of the previous work. We then further designed and optimized an efficient microstructure of loop unrolling and pipeline for the gridding accelerator, and the designed system simulation was done to confirm the performance of our structure. The result shows that the acceleration ratio is 5.48 compared to the result tested on software in gridding parts. Hence, our approach enables efficient acceleration of the faceting algorithm on FPGAs with high performance to meet the computational constraints of SKA as a representative vast sensor array. View Full-Text
Keywords: SKA; FPGA; cloud computing; big data technologies; phase rotation; gridding SKA; FPGA; cloud computing; big data technologies; phase rotation; gridding
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MDPI and ACS Style

Song, Y.; Zhu, Y.; Nan, T.; Hou, J.; Du, S.; Song, S. Accelerating Faceting Wide-Field Imaging Algorithm with FPGA for SKA Radio Telescope as a Vast Sensor Array. Sensors 2020, 20, 4070. https://doi.org/10.3390/s20154070

AMA Style

Song Y, Zhu Y, Nan T, Hou J, Du S, Song S. Accelerating Faceting Wide-Field Imaging Algorithm with FPGA for SKA Radio Telescope as a Vast Sensor Array. Sensors. 2020; 20(15):4070. https://doi.org/10.3390/s20154070

Chicago/Turabian Style

Song, Yuefeng, Yongxin Zhu, Tianhao Nan, Junjie Hou, Sen Du, and Shijin Song. 2020. "Accelerating Faceting Wide-Field Imaging Algorithm with FPGA for SKA Radio Telescope as a Vast Sensor Array" Sensors 20, no. 15: 4070. https://doi.org/10.3390/s20154070

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