VTR 7.0
Bibliographic record
Abstract
Exploring architectures for large, modern FPGAs requires sophisticated software that can model and target hypothetical devices. Furthermore, research into new CAD algorithms often requires a complete and open source baseline CAD flow. This article describes recent advances in the open source Verilog-to-Routing (VTR) CAD flow that enable further research in these areas. VTR now supports designs with multiple clocks in both timing analysis and optimization. Hard adder/carry logic can be included in an architecture in various ways and significantly improves the performance of arithmetic circuits. The flow now models energy consumption, an increasingly important concern. The speed and quality of the packing algorithms have been significantly improved. VTR can now generate a netlist of the final post-routed circuit which enables detailed simulation of a design for a variety of purposes. We also release new FPGA architecture files and models that are much closer to modern commercial architectures, enabling more realistic experiments. Finally, we show that while this version of VTR supports new and complex features, it has a 1.5× compile time speed-up for simple architectures and a 6× speed-up for complex architectures compared to the previous release, with no degradation to timing or wire-length quality.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.184 | 0.085 |
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".