Extending Memory Compatibility with Yosys Front-End in VTR Flow
Bibliographic record
Abstract
Verilog-to-routing (VTR) is an open source Computer Aided Design (CAD) framework that is widely used for research purposes. VTR provides researchers with a comprehensive set of benchmarks and FPGA architectures to test, compare and verify their designs and CAD algorithms. VTR employs Odin, Yosys and a combination of Yosys+Odin as its front-ends for elaborating digital designs written in Verilog. Yosys is a standalone synthesis framework that is maintained separately. In order to take advantage of Yosys' latest upgrades, it is essential to upgrade VTR in tandem with Yosys to keep up with its latest changes. In our study, we investigated the integration of the latest available version of Yosys into VTR. In the course of this research, we encountered a challenge stemming from a memory incompatibility in newer versions of Yosys. Our primary research question became how to effectively facilitate netlist conversion from Yosys to Odin to overcome this incompatibility issue. We then showcase how this upgrade affects our benchmark suite, highlighting the notable changes in circuit quality and the tool performance. Our post-upgrade evaluations reveal that the STA (static timing analysis) time of placement and the placement time itself exhibited improvements of up to 11.53% and 10.43%, respectively.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".