A Robust Top-Down Dynamic Power Estimation Methodology for Delay Constrained Register Transfer Level Sequential Circuits
Bibliographic record
Abstract
We present a top-down dynamic power estimation methodology for delay constrained sequential circuits. The methodology works at the register transfer level (RT-Level), and applies to both structural and behavioral descriptions of circuits. The average power consumption of a circuit varies with the worst case cycle-time or frequency of operation. As the cycle-time is reduced, the increase in the capacitance of the circuit due to technology mapping and optimization is captured by our technique at the RT-Level using the principles of logical effort. Switching activity is obtained at the RT-Level visible nodes through RT-Level functional simulation. This information is utilized to approximate the activities at the remaining nodes of the circuit and combined with capacitance to estimate dynamic power. Power estimation results for RT-Level sequential circuits indicate good accuracy (average error<10%) with respect to the reference values obtained by detailed gate-level power analysis. The power consumed by a circuit varies with the target library and technology. Our methodology is parameterizable and the results obtained for different target libraries at 0.18 mum TSMC and 0.13 mum UMC technologies are consistent, indicating the robustness of our technique. The applicability of our methodology in design frameworks consisting of bottom-up techniques is also discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".