An Optimized Cost Flow Algorithm to Spread Cells in Detailed Placement
Bibliographic record
Abstract
Placement is an important and challenging step in VLSI physical design. The placement solution can significantly impact timing and routability. In sub-nanometric technology nodes, several restrictions have been imposed on the placement solutions. These restrictions make designing an optimized and legal solution very hard. Achieving optimized placement solutions is especially challenging in regions with high-density utilization. The quality of placement solution can significantly impact the final circuit implementation. In this work, we present a cell spreading algorithm to move cells out from high-density utilization regions. Our algorithm opens up new spaces in regions with high cell concentration. These spaces can then be exploited by detailed placement algorithms to further optimize the placement solution. The objective of our technique is to reduce area density utilization while considering cell displacement and circuit delay. The outcome of the proposed algorithm is to obtain a uniform distribution of cells in the placement area while having minimal effects on the delay. To achieve this goal, our proposed algorithm uses branch and cut, and network flow techniques. Experimental results on industrial and academic circuits illustrate that our proposed algorithm can minimize circuit delay (up to 25%), cell displacement (up to 17μ m ), dynamic power consumption (up to 5.3%), and leakage power (up to 15%).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".