Stay Flexible: A High-Performance FPGA NPU Overlay for Graph Neural Networks
Bibliographic record
Abstract
Graph neural networks (GNNs) are a class of deep learning (DL) models widely-used for learning latent representations of graph-structured data for a variety of node/graph-level prediction tasks. Real-time applications of GNNs are evolving in various domains such as 3D object detection from LiDAR point clouds in autonomous vehicles [1] and classifying collected data in particle physics colliders [2]. Typically, these use cases have stringent latency constraints but can still benefit from batch processing of multiple graphs from different input sources. Existing accelerators either rely on preprocessing input graphs [3], [4] or are extremely specialized streaming pipelines which are unable to support dynamically changing workloads for these applications [5]. In this work, we take a different approach by enhancing the neural processing unit (NPU) [6] to accelerate a wide variety of GNN models without sacrificing its flexibility, performance or ability to run any of its originally supported DL workloads (e.g. MLPs, RNNs, GRUs, LSTMs).
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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".