VTR 9: Open-Source CAD for Fabric and Beyond FPGA Architecture Exploration
Bibliographic record
Abstract
This work details the capabilities of a major new release of the Verilog-to-Routing (VTR) open source FPGA CAD tool flow. Enhancements include generalizations of VTR’s architecture modeling language and optimizers to enable a more diverse set of programmable routing fabrics, FPGAs with embedded hard Networks-on-Chip (NoCs) and three-dimensional 3D FPGA systems that leverage stacked silicon integration. The new Parmys logic synthesis flow improves language coverage and result quality, and the physical implementation flow includes a more efficient placement engine, floorplanning constraints to guide placement, the ability to perform single-stage (flat) routing to improve quality, and parallel routing algorithms to reduce CPU time. This release also includes new architecture captures of recent commercial devices (Xilinx’s 7-series and Altera’s Stratix 10) and new benchmark suites (Titanium25 and Hermes) to aid FPGA architecture investigation. Verilog language coverage is greatly improved with the new Parmys logic synthesis flow, enabling more designs to be used with VTR. Finally, the placement and routing engines have beeenbeen sped up by 4 \(\times\) and 2.2 \(\times\) vs. VTR 8, respectively, leading to an overall physical implementation flow CPU time reduction of 48% with better result quality on average compared to VTR 8.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.020 |
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".