A Deep-Learning Data-Driven Approach for Reducing FPGA Routing Runtimes
Bibliographic record
Abstract
Many researchers have identified the long run-time of Field Programmable Gate Array (FPGA) routing tools as a major barrier to the timely completion of designs. This paper presents a robust, data-driven method for reducing routing run-time that requires no changes to the routing algorithm and that preserves solution quality. The approach involves first predicting the global switch and wire-segment utilization for a given placement, and then pre-loading this information into the router changing the node pricing used by the negotiation-congested heuristic employed by the router. This foresight enables the router to make alternate decisions early on, resulting in faster convergence. The forecasting of routing-resource utilization is formulated as an image-translation problem and resolved using a convolutional encoder decoder with modified loss function. The deep-learning model is trained and tested using the large, modern Titan benchmarks. The proposed approach is applied to two state-of-the-art FPGA routers, AIR and Enhanced Pathfinder, and adds almost no computational overhead to either router. Empirical results show an average reduction in CPU runtimes of 17.3% and 44.3% for AIR and Enhanced Pathfinder, respectively, with no significant degradation in wirelength or critical-path delay.
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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".