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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".