Bibliographic record
Abstract
The open source Verilog-to-Routing (VTR) tool flow can produce legal placement solutions for arbitrarily complex FPGA architectures and is thus widely used for novel device development as well as CAD tool research. VTR’s versatility is enabled by both its robust device modeling capability and its use of pre-placement clustering to abstract away complexity and maintain scalability. Clustering is not always necessary; recent academic tools demonstrate that delaying or omitting it can improve result quality for some device architectures. By incorporating a variety of external placement tools, VTR can maintain both versatility and result quality as FPGAs scale and diversify. Tool developers can benefit as well from access to VTR; however, due to VTR’s complex, hierarchical device modeling, its place and route interface requires a level of detail and accuracy that is beyond the scope of many external tools. To lower the barrier to interoperability, we introduce a VTR Interface for Placement with Error Resilience (VIPER). VIPER constructs a complete, legal, VTR-compatible clustering and placement solution based on a simplified and potentially illegal input placement.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".