Tear Down The Wall: Unified and Efficient Intra-and Inter-Cluster Routing for FPGAs
Bibliographic record
Abstract
Routing is one of the most time-consuming phases of the FPGA CAD flow, and its quality of result greatly impacts design speed and whether a successful implementation is achieved with the fixed wiring resources available. Routing is also strongly affected by the FPGA programmable interconnect architecture, making data-driven algorithms crucial to explore new fabrics. The VTR CAD flow has historically split the routing problem into two parts: within the logic cluster and between clusters. This allows a flexible specification of the programmable interconnect of each of these components and also simplifies the routing problem by splitting it into two sub-problems. However, for some architectures, this split can significantly reduce result quality, so in this work, we develop a single-stage router, run-flat, that can simultaneously optimize the intra-cluster and inter-cluster routing. We show that this new router increases the generality of architectures VTR can target and improves the quality of results at the cost of a run time increase. We detail router enhancements that reduce memory by removing non-essential nodes from the routing architecture, reduce run time by accurately ranking partial routing choices with an enhanced lookahead and efficiently resolve resource overuse at choke points with a new net-aware congestion model. Over the VTR benchmarks on an architecture with partial crossbars within the logic clusters, run-flat reduces the minimum channel width by 14% and critical path delay by 2% on average at the cost of 2.7 xthe router run time. On the Titan benchmark suite on a Stratix-IV-like architecture, run-flat reduces wirelength by 6% while taking 1.36 x the router run time.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".