fam_synthesis
Science Score: 49.0%
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Found 6 DOI reference(s) in README -
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Low similarity (12.1%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: Jingyi-li
- License: mit
- Language: Tcl
- Default Branch: main
- Size: 1.62 GB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 1
- Releases: 1
Metadata Files
README.md
FAM_Synthesis
This work corresponds to the ACM Transactions on Reconfigurable Technology and Systems Paper: https://dl.acm.org/doi/abs/10.1145/3567429
Citation
If you want to use this code please cite our paper:
@article{10.1145/3567429,
author = {Li, Carol Jingyi and Li, Xiangwei and Lou, Binglei and Jin, Craig T. and Boland, David and Leong, Philip H.W.},
title = {Fixed-Point FPGA Implementation of the FFT Accumulation Method for Real-Time Cyclostationary Analysis},
year = {2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {1936-7406},
url = {https://doi.org/10.1145/3567429},
doi = {10.1145/3567429},
abstract = {The spectral correlation density (SCD) is an important tool in cyclostationary signal detection and classification. Even using efficient techniques based on the fast Fourier transform (FFT), real-time implementations are challenging because of the high computational complexity. A key dimension for computational optimization lies in minimizing the wordlength employed. In this paper, we analyze the relationship between wordlength and signal-to-quantization noise in fixed-point implementations of the SCD function. A canonical SCD estimation algorithm, the FFT accumulation method (FAM) using fixed-point arithmetic is studied. We derive closed-form expressions for SQNR and compare them at wordlengths ranging from 14 to 26 bits. The differences between the calculated SQNR and bit-exact simulations are less than 1 dB. Furthermore, an HLS-based FPGA design is implemented on a Xilinx Zynq UltraScale+ XCZU28DR-2FFVG1517E RFSoC. Using less than 25% of the logic fabric on the device, it consumes 7.7 W total on-chip power and has a power efficiency of 12.4 GOPS/W, which is an order of magnitude improvement over an Nvidia Tesla K40 graphics processing unit (GPU) implementation. In terms of throughput, it achieves 50 MS/sec, which is a speedup of 1.6 over a recent optimized FPGA implementation.},
note = {Just Accepted},
journal = {ACM Trans. Reconfigurable Technol. Syst.},
month = {oct},
keywords = {FAM, HLS, SCD, FPGAs., quantization error}
}
and cite it using the following DOI: 10.5281/zenodo.7679339
Please also reference the included citation.cff file for more information.
Results
Results of the paper can be reproduced from FAM_Synthesis/Figures
Figure 6 & 12
Run FAMSynthesis/Figure/Resultsqnr.m
Figure 13
Run FAMSynthesis/Figures/Resultcheck.m
Figure 14
Run FAMSynthesis/Figures/Resultdsp.m
Figure 17
Run FAMSynthesis/Figures/Resultutilize.m

Table 3
Run FAMSynthesis/Figures/Resultopti.m
Owner
- Name: Jingyi.li
- Login: Jingyi-li
- Kind: user
- Repositories: 2
- Profile: https://github.com/Jingyi-li
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