RTL delay macro-modeling with V<inf>t</inf> and V<inf>dd</inf> variability
Bibliographic record
Abstract
Recent low-power design utilizes a variety of approaches for Vddand Vtcontrol to reduce dynamic and leakage power. It is important to be able to explore various low-power design options at a high-level early in the design process. Furthermore, process variation is becoming large and greatly affects the power and delay results. In particular, the delay analysis becomes very complicated and time-consuming with existing tools. This paper proposes a new efficient RTL delay macro-model to address these recent problems. The goal is to provide transistor-level accuracy at the RTL level with Vtand Vddvariability. It also includes the ability to handle PVT variations. The validation of the model is demonstrated by comparison with a circuit simulator and a timing verification tool. The experiments show this macro-model predicts the delay for variable Vddand Vtwith an accuracy of ±5% against HSPICE™ and ±10% against PrimeTime™ for a number of ITC'99 benchmark circuits.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".