Maximum Circuit Activity Estimation Using Pseudo-Boolean Satisfiability
Bibliographic record
Abstract
With lower supply voltages, increased integration densities and higher operating frequencies, power grid verification has become a crucial step in the very large-scale integration design cycle. The accurate estimation of maximum instantaneous power dissipation aims at finding the worst-case scenario where excessive simultaneous switching could impose extreme current demands on the power grid. This problem is highly input-pattern dependent and is proven to be NP-hard. In this paper, we capitalize on the compelling advancements in satisfiability (SAT) solvers to propose a pseudo-Boolean SAT-based framework that reports the input patterns maximizing circuit activity, and consequently peak dynamic power, in combinational and sequential circuits. The proposed framework is enhanced to handle unit gate delays and output glitches. In order to disallow unrealistic input transitions, we show how to integrate input constraints in the formulation. Finally, a number of optimization techniques, such as the use of gate switching equivalence classes, are described to improve the scalability of the proposed method. An extensive suite of experiments on ISCAS85 and ISCAS89 circuits confirms the robustness of the approach compared to simulation-based techniques and encourages further research for low-power solutions using Boolean SAT.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".