A Multiobjective Cooptimization of Buffer and Wire Sizes in High-Performance Clock Trees
Bibliographic record
Abstract
Clock buffer and wire sizing are intertwined problems that also greatly impact power consumption and skew in clock trees. Due to their complexity, they are often solved separately, leading to suboptimal solutions. In this brief, we propose a new formulation for cooptimization of buffer and wire sizes for high-performance clock trees. Using the proposed cooptimization of buffer and wire sizes, we are able to minimize a combination of both power and skew. The variation-aware experiments show that, by applying the proposed formulation, power and skew for all tested clock trees are improved. On average, we achieve a reduction of 57% in power and 50 ps in skew. We also show that our solutions are Pareto optimal where power and skew cannot be further reduced simultaneously and they provide a balanced tradeoff between power and skew.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".