Decimator systolic arrays design space exploration for multirate signal processing applications
Bibliographic record
Abstract
This study presents a new systolic array structure for a decimator that merges the antialiasing finite impulse response (FIR) filter with the downsampler. The development of the structure is based on a systematic methodology. Using this methodology, a dependence graph for the decimator was obtained that combined the antialiasing filter and the downsampler. Different data scheduling and projection operations were developed to obtain different proposed designs. Six systolic array design options were obtained and evaluated. The fastest design was selected for hardware implementation and compared with the other two well known decimator designs; namely, conventional design, in which the antialiasing filter is followed by a downsampling and the polyphase design, in which a commutator is followed by the polyphase antialiasing filter. Field‐programmable gate array implementations for the proposed and the other two designs confirm that the proposed decimator implementation outperforms in terms of area, speed, and power as the decimation factor increases regardless of the number of FIR filter coefficients.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 0.001 |
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".