Bibliographic record
Abstract
CAD tool designers are always searching for more benchmark circuits to stress their software. In this article we present a heuristic method to generate benchmark circuits specially suited for incremental place-and-route tools. The method removes part of a real circuit and replaces it with an altered version of the same circuit to mimic an incremental design change. The alteration consists of two steps: mutate followed by perturb . The perturb step exactly preserves as many circuit characteristics as possible. While perturbing, reproduction of interconnect locality, a characteristic that is difficult to measure reliably or reproduce exactly, is controlled using a new technique, ancestor depth control (ADC). Perturbing with ADC produces circuits with postrouting properties that match the best techniques known to-date. The mutate step produces targetted mutations resulting in controlled changes to specific circuit properties (while keeping other properties constant). We demonstrate one targetted mutation heuristic, scale, to significantly change circuit size with little change to other circuit characteristics. The method is simple enough for inclusion in a CAD tool directly, and fast enough for use in on-the-fly benchmark generation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.018 | 0.004 |
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".