GraphNoC: Graph Neural Networks for Application-Specific FPGA NoC Performance Prediction
Bibliographic record
Abstract
We can democratize design of FPGA Network-on-Chips by replacing slow and expensive conventional NoC benchmarking tools with highly accurate and fast Graph Neural Networks based models. FPGA reconfigurability allows for tuning and designing of NoCs specific to the application being implemented on the FPGA, a facility not afforded to ASIC NoCs. However, such application-specific NoC designs can require thousands of incremental updates and customization to the NoC design, with each resulting NoC configuration needing benchmarking for packet performance to guide the design process. Additionally, each of these benchmark runs can take up to minutes with conventional tools like RTL simulation for modest packet trace lengths. As a result, tuning and design of a NoC even for a single FPGA application can last up to days, presenting a critical bottleneck to developer efficiency and iteration speed. We address this by presenting a framework to encode any FPGA NoC and any FPGA application traffic into graphs, called GraphNoC. We create a dataset of these graphs, comprising of different FPGA NoCs and applications. We use this dataset to train GNNs, including foundation models, to predict NoC routing latencies that can accelerate benchmarking run-times by up to$148 \times(506 \times$using GPU) with prediction top-20 accuracies up to 97.2 %. We also show these GNNs can accelerate end-to-end FPGA application-specific NoC design by up to$4.3 \times$($37 \times$using GPU) while regressing final NoC latency by only 30 cycles.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".