A novel approach based on genetic algorithm for pipelining of recursive filters
Why this work is in the frame
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Bibliographic record
Abstract
Look-ahead pipelining is an approach for pipelining IIR filters by adding cancelling poles and zeros to the transfer function. Several modifications of the original look-ahead pipelining have been presented by different authors, Soderstrand et al. (1995) and Lim and Lin (1992), in order to obtain more stable filters with smaller hardware. In this paper a method based on Genetic Algorithm (GA) for optimization of the look-ahead pipelining technique is presented. The presented technique offers improvement over the previously published techniques both in reducing the hardware complexity, and the magnitude of superfluous poles. Finally by allowing extra hardware in the form of shift and add rather than full multipliers further improvements in the magnitude of superfluous poles is obtained with small additional hardware.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it