Towards a Reference Place and Route Flow for Academic Research
Bibliographic record
Abstract
Due to the complexity of contemporary circuits, physical synthesis has become a crucial step for achieving design closure. The placement of cells direct impacts the routing solution. For example, a region with a high cell density can lead to pin access issues in detailed routing. Therefore, small inefficiencies in the placement solution can be boosted during routing, which has a negative impact on design quality and convergence. Unfortunately, most academic research works evaluate the results only in the target step without considering the complete place and route flows. In this work, we experimentally explored different flows built up from academic placers and routers to find which one leads to the best overall results so that researchers can use them as a reference. In order to evaluate those flows, we used the ISPD 2018 and ISPD 2019 CAD Contest benchmarks, which are the most realistic academic benchmarks available with placement and routing information. Considering the evaluator reports, no combination of tools achieved the best result for all circuits. Nevertheless, the flow Contest placement + CUGR + TritonRoute achieved the best results in fifteen out of twenty benchmarks.
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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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.016 |
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".