Improving the Fast Fourier Transform for Space and Edge Computing Applications with an Efficient In-Place Method
Satellite and edge computing designers develop algorithms that restrict resource utilization and execution time. Among these design efforts, optimizing Fast Fourier Transform (FFT), key to many tasks, has led mainly to in-place FFT-specific hardware accelerators. Aiming at improving the FFT performa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Software |
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| Online Access: | https://www.mdpi.com/2674-113X/4/2/11 |
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| author | Christoforos Vasilakis Alexandros Tsagkaropoulos Ioannis Koutoulas Dionysios Reisis |
| author_facet | Christoforos Vasilakis Alexandros Tsagkaropoulos Ioannis Koutoulas Dionysios Reisis |
| author_sort | Christoforos Vasilakis |
| collection | DOAJ |
| description | Satellite and edge computing designers develop algorithms that restrict resource utilization and execution time. Among these design efforts, optimizing Fast Fourier Transform (FFT), key to many tasks, has led mainly to in-place FFT-specific hardware accelerators. Aiming at improving the FFT performance on processors and computing devices with limited resources, the current paper enhances the efficiency of the radix-2 FFT by exploring the benefits of an in-place technique. First, we present the advantages of organizing the single memory bank of processors to store two (2) FFT elements in each memory address and provide parallel load and store of each FFT pair of data. Second, we optimize the floating point (FP) and block floating point (BFP) configurations to improve the FFT Signal-to-Noise (SNR) performance and the resource utilization. The resulting techniques reduce the memory requirements by two and significantly improve the time performance for the overall prevailing BFP representation. The execution of inputs ranging from 1K to 16K FFT points, using 8-bit or 16-bit as FP or BFP numbers, on the space-proven Atmel AVR32 and Vision Processing Unit (VPU) Intel Movidius Myriad 2, the edge device Raspberry Pi Zero 2W and a low-cost accelerator on Xilinx Zynq 7000 Field Programmable Gate Array (FPGA), validates the method’s performance improvement. |
| format | Article |
| id | doaj-art-391aed9b0e9448aba869a14b5b618174 |
| institution | OA Journals |
| issn | 2674-113X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Software |
| spelling | doaj-art-391aed9b0e9448aba869a14b5b6181742025-08-20T02:21:50ZengMDPI AGSoftware2674-113X2025-05-01421110.3390/software4020011Improving the Fast Fourier Transform for Space and Edge Computing Applications with an Efficient In-Place MethodChristoforos Vasilakis0Alexandros Tsagkaropoulos1Ioannis Koutoulas2Dionysios Reisis3Electronics Lab, Physics Department, National & Kapodistrian University of Athens, 157 84 Athens, GreeceElectronics Lab, Physics Department, National & Kapodistrian University of Athens, 157 84 Athens, GreeceElectronics Lab, Physics Department, National & Kapodistrian University of Athens, 157 84 Athens, GreeceElectronics Lab, Physics Department, National & Kapodistrian University of Athens, 157 84 Athens, GreeceSatellite and edge computing designers develop algorithms that restrict resource utilization and execution time. Among these design efforts, optimizing Fast Fourier Transform (FFT), key to many tasks, has led mainly to in-place FFT-specific hardware accelerators. Aiming at improving the FFT performance on processors and computing devices with limited resources, the current paper enhances the efficiency of the radix-2 FFT by exploring the benefits of an in-place technique. First, we present the advantages of organizing the single memory bank of processors to store two (2) FFT elements in each memory address and provide parallel load and store of each FFT pair of data. Second, we optimize the floating point (FP) and block floating point (BFP) configurations to improve the FFT Signal-to-Noise (SNR) performance and the resource utilization. The resulting techniques reduce the memory requirements by two and significantly improve the time performance for the overall prevailing BFP representation. The execution of inputs ranging from 1K to 16K FFT points, using 8-bit or 16-bit as FP or BFP numbers, on the space-proven Atmel AVR32 and Vision Processing Unit (VPU) Intel Movidius Myriad 2, the edge device Raspberry Pi Zero 2W and a low-cost accelerator on Xilinx Zynq 7000 Field Programmable Gate Array (FPGA), validates the method’s performance improvement.https://www.mdpi.com/2674-113X/4/2/11FFTin-place computingedge computingsatellite applications |
| spellingShingle | Christoforos Vasilakis Alexandros Tsagkaropoulos Ioannis Koutoulas Dionysios Reisis Improving the Fast Fourier Transform for Space and Edge Computing Applications with an Efficient In-Place Method Software FFT in-place computing edge computing satellite applications |
| title | Improving the Fast Fourier Transform for Space and Edge Computing Applications with an Efficient In-Place Method |
| title_full | Improving the Fast Fourier Transform for Space and Edge Computing Applications with an Efficient In-Place Method |
| title_fullStr | Improving the Fast Fourier Transform for Space and Edge Computing Applications with an Efficient In-Place Method |
| title_full_unstemmed | Improving the Fast Fourier Transform for Space and Edge Computing Applications with an Efficient In-Place Method |
| title_short | Improving the Fast Fourier Transform for Space and Edge Computing Applications with an Efficient In-Place Method |
| title_sort | improving the fast fourier transform for space and edge computing applications with an efficient in place method |
| topic | FFT in-place computing edge computing satellite applications |
| url | https://www.mdpi.com/2674-113X/4/2/11 |
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