An effective clustering algorithm for mixed-size placement
Bibliographic record
Abstract
Placement is a crucial step for the VLSI circuit physical design and it has a deep impact on the overall circuit performance. Numerous clustering techniques have been proposed and applied to placement to deal with the increasing circuit sizes and complexity. In this paper, an effective clustering algorithm for mixed-size placement is presented. This technique uses local cell connectivity information to identify all potential clusters, but finalizes clusters globally. The effectiveness of the proposed clustering technique is verified by empirical tests on ICCAD04 and ISPD05 benchmark circuits. Specifically, 4 major academic placers, including Capo10.1, FengShui5.1, mPL6 and NTUPlace3-LE, are tested by using the proposed clustering technique as a preprocessing step. The overall experimental results show that for ICCAD04 benchmarks, the proposed clustering technique consistently improves all of the placers' performance by 2% to 5% on average in term of the pin-to-pin half perimeter wire length, with comparable or lower runtime. For ISPD05 benchmarks, the proposed clustering technique shows promising results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".