A Modular Heterogeneous Stack for Deploying FPGAs and CPUs in the Data Center
Bibliographic record
Abstract
In this work we present a heterogeneous deployment stack, calledGalapagos, that includes the abstraction of individual nodes (FPGAsand CPUs), the communication protocols between nodes and theorchestration and connection of these nodes into clusters. The stackwe create is also highly modular, allowing users to explore a designspace in the implementation of their cluster such as different net-work protocols or communication layers. The communication layerwe have currently implemented within our hardware stack, calledHUMboldt, handles heterogeneous communication between multi-ple FPGAs and CPUs. We implementHUMboldtusing High-LevelSynthesis (HLS) to ensure functional portability of communicatingkernels, allowing us to prototype hardware kernels in software. Ourresults have shown that our modular approach to this heterogeneousdeployment stack has introduced very little area and latency over-head in the FPGAs and can still perform at line-rate, bottleneckedsolely by the network links connecting the nodes. Our results alsohighlight the scalability of our design as our performance remainslimited by the network links when the cluster size increases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".