Low-power multi-threshold CMOS circuits optimization and CAD tool design
Bibliographic record
Abstract
As technology scales into the Deep Sub-Micron regime (DSM), standby subthreshold leakage power increases exponentially with the reduction of the threshold voltage. Therefore, effective leakage minimization techniques are a necessity. In addition, for a true low-power solution, it needs to be integrated into the principal design environment. In this thesis, two genetic algorithms are implemented to efficiently solve the Bin-Packing (BP) and the Set-Partitioning (SP) problems in the gate-clustering MTCMOS technique. Also, two design techniques are proposed to effectively solve the sleep transistor sizing problem in MTCMOS circuits. The introduced First-Fit (FF) and Set-Covering (SC) approaches achieve a lower leakage at an order of magnitude reduction in the CPU time, compared to those of other techniques in the literature. In addition, an automatic MTCMOS design environment is devised and integrated into the Canadian Microelectronics Corporation (CMC) digital ASIC design flow.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".