Total power optimization combining placement, sizing and multi-Vt through slack distribution management
Bibliographic record
Abstract
Power dissipation is quickly becoming one of the most important limiters in nanometer IC design for leakage increases exponentially as the technology scaling down. However, power and timing are often conflicting objectives during optimization. In this paper, we propose a novel total power optimization flow under performance constraint. Instead of using placement, gate sizing, and multiple-Vt assignment techniques independently, we combine them together through the concept of slack distribution management to maximize the potential for power reduction. We propose to use the linear programming (LP) based placement and the geometric programming (GP) based gate sizing formulations to improve the slack distribution, which helps to maximize the total power reduction during the Vt-assignment stage. Our formulations include important practical design constraints, such as slew, noise and short circuit power, which were often ignored previously. We tested our algorithm on a set of industrial-strength manually optimized circuits from a multi-GHz 65 nm microprocessor, and obtained very promising results. To our best knowledge, this is the first work that combines placement, gate sizing and Vt swapping systematically for total power (and in particular leakage) management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".