Library-free logic synthesis of high performance ip blocks for nanometer technologies
Bibliographic record
Abstract
This thesis introduces a new approach to library-free logic synthesis (LFLS) of intellectual property (IP) blocks, with corresponding developments related to the generation, modeling, and optimization of virtual cells. Template cell structures for the virtual cell topology are first chosen as being AND-OR-INVERT and OR-AND-INVERT in function and implemented in a static CMOS configuration. These templates were then modeled using an enhanced logical effort model (ELEM), developed to address ultra-deep sub-micron (UDSM) effects. This model was implemented in MATLAB and compared with HSPICE simulations across 4 nanometer technology nodes; 90nm, 65nm, 45nm and 32nm, with good accuracy ranging from an average of 3.3% for simple gates and 6.9% for complex gates. This model was then used to determine the ideal partitioning stack length criteria, or maximum number of NMOS and PMOS transistors connected serially, in order to ensure complex gate delay optimization vs. a simple cell implementation of the same function. The results show that depending on the technology node and template cell structure, more than one stack length criteria is possible to provide this optimization, a result not addressed in any of the literature. Based on this result, and number of derived and tested heuristics, a new partitioning algorithm, dubbed Complementary Logic Partitioning or CLP, was developed. The cumulative rules and requirements for CLP was then automated into the CLP tool, and compared with the Synopsys Design Compiler software, as well as other library-free logic synthesis techniques. The results show that compared to the other LFLS techniques, CLP performs adequately, meeting or exceeding the alternative approach by an average of 30% in delay, with fluctuations in area comparison. In relation to the Synopsys software and the standard cell approach, the CLP tool decreases the delay of the benchmark designs by an average of 29%, with an average area increase of 18% when compared with Synopsys. Finally, the ability of performing transistor size optimization was investigated. By enhancing a current process for optimization to address complex cells, an average energy savings of 25% and area savings of 30% is demonstrated, requiring only an average increase of 4% in delay.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".