Integrating Machine-Learning Probes into the VTR FPGA Design Flow
Bibliographic record
Abstract
This paper proposes a set of Machine-Learning (ML) probes that can be used at the placement step within the Verilog-to-Routing (VTR) tool. The proposed probes can pro-vide real-time feedback to the VTR placer guiding it towards more “router-friendly” placement solutions that result in the router performing fewer computationally expensive rip-up and re-route operations. In addition to enabling the previous strategies for reducing routing runtimes, the proposed probes can also be used to speed up architecture exploration by providing estimates of interconnect resource utilization on the Field Programmable Gate Array (FPGA) without incurring the computational cost of actually performing routing. Re-sults obtained indicate that the proposed ML probes not only improve upon all the VTR estimates in terms of wirelength, critical path delay and segmented wire utilization but also reduce the routing time of the tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".