Bibliographic record
Abstract
This paper presents a new technology mapper, WireMap. The mapper uses an edge flow heuristic to improve the routability of a mapped design. The heuristic is applied during the iterative mapping optimization to reduce the total number of pin-to-pin connections (or edges). The average edge reduction of 9.3% is achieved while maintaining depth and LUT count of state-of-the-art technology mapping. Placing and routing the resulting netlists leads to an 8.5% reduction in the total wire length, a 6.0% reduction in minimum channel width, and a 2.3% reduction in critical path delay. Applying WireMap has an additional advantage of reducing an average number of inputs of LUTs without increasing the total LUT count and depth. The percentages of 5- and 6-LUTs in a typical design are reduced, while the percentages of 2-, 3-, and 4-LUTs are increased. These smaller LUTs can be merged into pairs and implemented using the dual output LUT structure found in commercial FPGAs. WireMap leads to 9.4% fewer dual-output LUTs after merging
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.123 | 0.055 |
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".