Toward Quality EDA Tools and Tool Flows Through High-Performance Computing
Bibliographic record
Abstract
As the scale and complexity of VLSI circuits increase, electronic design automation (EDA) tools become much more sophisticated and are held to increasing standards of quality. New-generation EDA tools must work correctly on a wider range of inputs, have more internal states, take more effort to develop, and offer fertile ground for programming mistakes. Ensuring quality of a commercial tool in realistic design flows requires rigorous simulation, non-trivial computational resources, accurate reporting of results and insightful analysis. However, time-to-market pressures encourage EDA engineers and chip designers to look elsewhere. Thus, the recent availability of cheap Linux clusters and Grids shifts the bottleneck from hardware to logistical tasks, i.e., the speedy collection, reporting and analysis of empirical results. To be practically feasible, such tasks must be automated; they leverage high-performance computing to improve EDA tools. In this work we outline a possible infrastructure solution, called bX, explore relevant use models and describe our computational experience. In a specific application, we use bX to automatically build Pareto curves required for accurate performance analysis of randomized algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".