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
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Repository

Basic Info
  • Host: GitHub
  • Owner: Jingyi-li
  • License: mit
  • Language: Tcl
  • Default Branch: main
  • Size: 1.62 GB
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Created about 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

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

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Figure 13

Run FAMSynthesis/Figures/Resultcheck.m

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Figure 14

Run FAMSynthesis/Figures/Resultdsp.m

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Figure 17

Run FAMSynthesis/Figures/Resultutilize.m Screenshot from 2022-08-22 18-17-29

Table 3

Run FAMSynthesis/Figures/Resultopti.m

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  • Name: Jingyi.li
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