Paired Faster Fft: Grigoryan Fft Implementation And Performance On Xilinx Fpgas And Tms Dsps
Bibliographic record
Abstract
Discrete Fourier Transform is a principal mathematical method for the frequency analysis and has wide applications in Engineering and Sciences. Because the DFT is so ubiquitous, fast methods for computing DFT have been studied extensively, and continuous to be an active research. The way of splitting the DFT gives out various fast algorithms. In this paper, we present the implementation of two fast algorithms for the DFT for evaluating their performance. One of them is the popular radix-2 Cooley-Tukey fast Fourier transform algorithm (FFT) [1] and the other one is the Grigoryan FFT based on the splitting by the paired transform [2]. We evaluate the performance of these algorithms by implementing them on the Xilinx Virtex-II Pro [6], Virtex-4[9] and Virtex-5[7] FPGAs, by developing our own FFT processor architectures. We have evaluated the performances also by implementing on Texas Instruments fixed point DSP processors: TMS320C5416[17], TMS320C6748[17], TMS320C5515[17]. Finally we show that the Grigoryan FFT is working faster than the Cooley-Tukey FFT, consequently it is useful for higher sampling rates. Operating at higher sampling rates is a challenge in DSP applications. We proved that on Xilinx FPGAs and TMS DSPs, the Grigoryan FFT is performing at most 1.358 and 1.7 times faster than the Cooley-Tukey FFT respectively. We also confirm that for the same architectures Virtex-5 platform is better platform for implementing the Grigoryan FFT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".