Abstract
While many Fast Fourier Transform (FFT) architectures have been presented for computing real-valued FFT (RFFT), which of these architectures is best suited for low-throughput applications such as biomedical signals which are typically sampled between 256 Hz and 1 kHz remains unclear. This paper implements and compares throughput, resources, and energy consumption of three different hardware architectures for real-valued FFT algorithms using Xilinx Zynq-7000 FPGA. The RFFT architectures exploit the conjugate symmetry property of the real signals, thereby eliminating about half of the computations compared to a complex FFT. The three FFT architectures investigated in this paper include: single processing element (SPE), pipelined, and in-place. It is shown that, for a 256-point RFFT, using FPGA, the in-place architectures require the least device resources when compared to the pipelined architectures, while the throughput of the pipelined architectures is approximately 8 times that of the in-place architecture.
Original language | English (US) |
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Title of host publication | 2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 112-115 |
Number of pages | 4 |
ISBN (Electronic) | 9781665424615 |
DOIs | |
State | Published - Aug 9 2021 |
Externally published | Yes |
Event | 2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 - Virtual, East Lansing, United States Duration: Aug 9 2021 → Aug 11 2021 |
Publication series
Name | Midwest Symposium on Circuits and Systems |
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Volume | 2021-August |
ISSN (Print) | 1548-3746 |
Conference
Conference | 2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 |
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Country/Territory | United States |
City | Virtual, East Lansing |
Period | 8/9/21 → 8/11/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Fast Fourier Transform (FFT)
- Real-Valued FFT
- biomedical signals
- in-place
- pipelined
- real-valued signals