A novel particle swarm optimization for high-level synthesis of digital filters
Bibliographic record
Abstract
This paper presents a novel discrete particle swarm optimization (PSO) technique for the high-level synthesis of digital filter data-paths. In this technique, the cost associated with the final digital filter data-path is minimized for obtaining combined area-cum-time optimal digital filter data-paths subject to user-specified constraints on the number of the required arithmetic functional units. In the proposed technique, the digital filter data-path encoding is achieved by combining the information regarding the operation scheduling together with the information regarding the allocation and binding of operations to arithmetic functional units into a single particle. The scheduling, and allocation and binding information form the coordinate values of the particles in PSO. The salient feature of the resulting PSO technique is its fast convergence speed, achieved by ensuring that the (random) movement of the particles in the search space in the course of optimization are automatically guaranteed to preserve the data-dependency relationships in the original digital filter signal flow-graph without any recourse to backtracking. The usefulness of the proposed PSO technique is demonstrated through the application of it to the high-level synthesis of a benchmark elliptic wave digital filter. It is observed that the application of the PSO leads to substantially faster convergence speeds as compared to the corresponding genetic algorithms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".