A hybrid evolutionary analogue module placement algorithm for integrated circuit layout designs
Bibliographic record
Abstract
This paper presents an integrated approach of simulated annealing (SA) and genetic algorithm (GA) for the analogue module placement in mixed-signal integrated circuit layout designs. The proposed algorithm follows the optimization flow of a normal GA controlled by the methodology of SA. The bit-matrix chromosomal representation is employed to describe the location and the orientation of modules. Compared with the conventional bit-string representation, the proposed chromosomal representation tends to significantly improve the search efficiency. In addition, a slide-based flat scheme is developed to transform an absolute co-ordinate placement of modules to a relative placement. In this way, the symmetry constraints imposed on analogue very large scale integration circuits can be easily fulfilled in the placement run. Use of a radiation-decoder can also drastically shrink the configuration space without degrading search opportunities. The proposed algorithm has been tested with several example circuits. The experiments show this promising algorithm makes the better performance than the simpler SA or GA approaches working alone, and the quality of the automatically generated layouts is comparable to those done manually. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".