Guiding FPGA Detailed Placement via Reinforcement Learning
Bibliographic record
Abstract
Detailed Placement (DP) is an important, but time-consuming, optimization step within the Field Programmable Gate Array (FPGA) design flow. Given a global placement, DP seeks to refine the global placement to improve the success of the subsequent routing step. In this paper, we show how Reinforcement Learning (RL) can be used to significantly reduce DP runtimes while maintaining Quality-of-Result (QoR). We develop 3 different RL models based on Tabular Q-Learning, Deep Q-Learning, and Actor-Critic. These models are evaluated by integrating them into GPlace3.0 – a state-of-the-art analytic FPGA placement tool – and tested using the 12 ISPD contest benchmarks. Our results show the models achieve total runtime improvements between 2x to 3.5x and similar QoR compared to GPlace3.0’s algorithmic-based detailed placer.
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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) | 0.004 | 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".