射电望远镜数字光谱仪中嵌套RNS的FFT电路

Hiroki Nakahara, Tsutomu Sasao, H. Nakanishi, K. Iwai, T. Nagao, Naoya Ogawa
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引用次数: 0

摘要

射电望远镜分析从天体接收的射频(RF)信号。它由天线、接收器和分光仪组成。光谱仪通过FFT运算将时域信号转换为频域信号。提出了一种基于嵌套剩余数系统(NRNS)的FFT电路。由于FFT运算是计算量最大的部分,因此实现高速FFT需要并行实现。我们使用FPGA来实现电路。FPGA由查找表(lut)和块ram (bram)组成。对于使用现有的FPGA库实现直接并行FFT,用于复杂乘法器的lut数量是瓶颈。为了减少FPGA中lut的数量,我们逐步增加动态范围。在这种情况下,需要使用NRNS2NRNS转换器,将较小的动态范围转换为较大的动态范围。我们在Xilinx公司的Virtex 7 FPGA上实现了所提出的NRNS FFT。与传统二进制FFT相比,RNS FFT虽然块ram (bram)数量增加了20.0 ~ 156.5%,但lut数量减少了42.4 ~ 47.8%,最大时钟频率提高了9.3 ~ 41.7%。使用这种技术,我们成功地在可用的FPGA上实现了满足所需尺寸和速度规格的FFT,因为过多的lut是二进制FFT的瓶颈。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An FFT Circuit Using Nested RNS in a Digital Spectrometer for a Radio Telescope
A radio telescope analyzes radio frequency (RF) signal received from celestial objects. It consists of an antenna, a receiver, and a spectrometer. The spectrometer converts the time domain signal into the frequency domain signal by an FFT operation. This paper proposes an FFT circuit based on nested residue number system (NRNS). Since the FFT operation is the most computationally intensive part, parallel implementation is necessary to realize a high-speed FFT. We used an FPGA to implement the circuit. The FPGA consists of look-up tables (LUTs) and block RAMs (BRAMs). For direct parallel FFT realization using an existing FPGA library, the number of LUTs for the complex multipliers is the bottleneck. To reduce the number of LUTs in an FPGA, we increase the dynamic range stage by stage. In this case, NRNS2NRNS converters that convert smaller dynamic range to larger dynamic range are necessary. We implemented the proposed NRNS FFT on the Xilinx Corp. Virtex 7 FPGA. Compared with a conventional binary FFT, although the number of block RAMs (BRAMs) was increased by 20.0-156.5%, in the RNS FFT, the number of LUTs was decreased by 42.4-47.8%and the maximum clock frequency was increased by 9.3-41.7%.With this technique, we successfully implemented an FFT that satisfied the required size and speed specifications on an available FPGA, since the excessive number of LUTs was the bottleneck of the binary FFT.
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