Parallel FPGA Routing with On-the-Fly Net Decomposition
Bibliographic record
Abstract
A high-quality routing algorithm is crucial to achieving high-speed FPGA designs, and it is one of the most timeconsuming steps in the FPGA CAD flow. Using multiple CPUs is one way to reduce route time. However, exploiting parallelism on the most performant algorithms incorporating negotiated congestion, directed searches, and incremental approaches has been challenging. We introduce two parallel routers extending the state-of-the-art PathFinder-based AIR router in VPR 8. The first is the baseline parallel router, based on the widely applied technique of recursively bi-partitioning the physical FPGA so nonoverlapping nets can be routed in parallel; however, scalability is limited by nets (often high-fanout) spanning large chip areas. The second router enhances the baseline by applying a new net decomposition method to enable fragments of nets to be routed in parallel for better scalability. For intra-cluster routing, Titan benchmarks, and eight threads, we obtain a speedup of 2.14× with the baseline and 2.38× with the net-decomposing router, compared to the latest VPR 8+ sequential router. On flat (singlestep) routing, the net-decomposing router achieves a speedup of 2.15× with eight threads. The routers are deterministic and serially equivalent, achieving wire length and critical path delay comparable to the sequential algorithm. The routers are being integrated into the open-source VTR framework, enabling the research community to build on this work.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".