Clock Distribution Architectures: A Comparative Study
Why this work is in the frame
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Bibliographic record
Abstract
This paper evaluates and compares different clock architectures such as mesh, tree and their hybrids, on several industrial designs. The goal of our study is to gain a quantitative understanding of engineering trade-offs between different architectures with respect to clock skew, latency, timing uncertainty, and power. This understanding will lead to guidelines for determining the best clock architecture for the design specification and constraints. To the best of our knowledge, no work has been published on evaluating and comparing these architectures on real industrial designs. Our study shows that mesh-based architectures are better than tree architectures for skew (< 1ps skew) and are more robust to variations (18% reduction in timing uncertainty as compared to tree). The power penalty associated with a mesh as compared to a tree was found to be between 10-40%. Use of multiple meshes can help reduce the power penalty
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it