Reshaping EDA for power
Bibliographic record
Abstract
Today's rising power densities have been widely cited as the foremost challenge to continued CMOS scaling. In fact, the current power crisis is reminiscent of the final days previous technologies, such as the once popular bipolar and NMOS technologies and even vacuum tubes. How CMOS technology will respond to the current power challenge to extend CMOS scaling to sub-90nm technology is an important question for designer and CAD tool developers alike. With aggressive scaling a number of new challenges have arisen, such as leakage control, heat removal and power supply distribution, that need to be addressed using new design techniques in conjunction with new CAD solutions.This panel brings together experts in circuit design and CAD tool development to discuss the current status of low-power design and provide opinions on what new EDA capabilities are most important in the power-constrained design era. For instance, how will power be distributed in a robust fashion in sub-90nm ICs, and what are the critical EDA analysis and optimization capabilities? What are the best techniques for leakage reduction, not only in standby modes, but also in the active mode? And how far will voltage scaling take us in attacking the dynamic power consumption issue? What will a power-centric design flow look like and how will it change the way we design ICs? The objective of the panel is to explore these issues and formulate a list of critical issues that need to be addressed by the EDA community to enable successful scaling of CMOS into the sub-90nm era.
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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 | 0.004 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".