Escaping the Academic Sandbox: Realizing VPR Circuits on Xilinx Devices
Bibliographic record
Abstract
This paper presents a new, open-source method for FPGA CAD researchers to realize their techniques on real Xilinx devices. Specifically, we extend the Verilog-To-Routing (VTR) suite, which includes the VPR place-and-route CAD tool on which many FPGA innovations have been based, to generate working Xilinx bitstreams via the Xilinx Design Language (XDL). Currently, we can faithfully translate VPR's heterogeneous packing and placement results into an exact Xilinx `map' netlist, which is then routed by its `par' tool. We showcase the utility of this new method with two compelling applications targeting a 40nm Virtex-6 device: a fair comparison of the area, delay, and CAD runtime of academia's state-of-the-art VTR How with a commercial, closed-source equivalent, along with a CAD experiment evaluated using physical measurements of on-chip power consumption and die temperature, over time. This extended How - VTR-to-Bitstream - is released to the community with the hope that it can enhance existing research projects as well as unlock new ones.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".