An automated power estimation and optimization methodology
Bibliographic record
Abstract
Power consumption is currently a very important criteria in digital system designs, however power analysis and optimization, especially at the gate level and lower levels of design abstraction, is tedious and complicated for most entry level designers. It requires several iterations of simulation and synthesis to generate and apply switching activity and it is also difficult to setup the various electronic design automation (EDA) tools. In this paper we discuss an automated power estimation and optimization flow, using two well-known EDA tools, that eliminates the laborious tasks of setting design parameters and configuring the EDA tools and thus significantly reduces the development time from several days to several hours. The proposed automated power estimation and optimization flow helps digital designers to estimate and optimize the power consumption of designs effortlessly, accurately, and effectively at gate level. The proposed power estimation and optimization flow also maximizes the capabilities of using available modern computational power in order to examine the effects of a wide range of design and implementation parameters on the power consumption, speed, and area complexity. A case study, the implementation of power efficient finite field multipliers, is shown.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".