Self-sorting FFT method eliminating trivial multiplication and suitable for embedded DSP processor
Bibliographic record
Abstract
The Discrete Fourier Transform (DFT) is a mathematical procedure at the core of processing inside a Digital Signal Processor. Speed and low complexity are crucial in the FFT process; they can be achieved by avoiding trivial multiplications through a proper handling of the input/output data and the twiddle factors. Accordingly, this paper presents an innovative approach for handling the input/output data efficiently by avoiding trivial multiplications. This approach consists of a simple mapping of the three indices (FFT stage, butterfly and element) to the addresses of the input/output data with their corresponding coefficient multiplier. A self-sorting algorithm that reduces the amount of memory accesses to the coefficient multipliers' memory can also reduce the computational load by avoiding all trivial multiplications. Compared with the most-recent work [5], performance evaluation in terms of the number of cycles on the general-purpose TMS320C6416 DSP shows a reduction of 29% (FFT of size 4096) and a 50% memory reduction to stock twiddle factors. The algorithm has also shown a speed gain of 24% on the FFTW platform for a FFT of size 4096.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".