Bibliographic record
Abstract
The emphasis in VLSI design has shifted from high speed to low power due to the proliferation of portable electronic systems. Many of the techniques have already been used in low power design with additional techniques emerging continuously at all levels. The goal of this work is to provide a comprehensive study of low-power circuit and design techniques using complementary metal-oxide-semiconductor (CMOS) technology. This will encompass aspects such as circuit design; transistor size, layout technique, cell topology, and circuit design for low power operation while paying particularly attention on the methodology of logic style. This thesis specifically deals with the comparison between static CMOS and complementary pass-transistor logic (CPL) styles, in a 0.35 mum CMOS technology, to determine the most efficient choice for low power design. The comparison study allows a selection procedure between static CMOS and CPL for low-power logic circuits, and provides a set of comparison results for use with other circuit design techniques.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .C66. Source: Masters Abstracts International, Volume: 41-04, page: 1150. Adviser: Graham Jullien. Thesis (M.A.Sc.)--University of Windsor (Canada), 2002.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.017 |
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".