Design Optimization Based on Diagnosis Techniques
Bibliographic record
Abstract
Logic optimization is the step of the VLSI design cycle where the designer performs modifications on the design obtained to satisfy di#erent constraints such as area, power or delay. In this paper we propose a novel ATPGbased optimization methodology that borrows from previous design error diagnosis and correction techniques. We also present examples and experiments that indicate that our approach has additional potential when compared to previous ATPG/simulation-based optimization methods. Andreas Veneris Magdy S. Abadir Ibrahim N. Hajj University of Toronto Motorola University of Illinois ECE Department 7700 W. Parmer ECE Department and CSL Toronto, ON M5S 3G4 Austin, TX 78729 Urbana, IL 61801 veneris@eecg.toronto.edu m.abadir@motorola.com hajj@uivlsi.csl.uiuc.edu 1 Logic Design Optimization The design of digital circuits usually starts with a behavioral description (specification) coded in some high level description language. At some point of the design cycle, logic synthesi...
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".