Exploring The Impact Of Switch Arity On Butterfly Fat Tree Fpga Nocs
Bibliographic record
Abstract
Overlay Networks-on-Chip (NoCs) for FPGAs based on the Butterfly-Fat Tree (BFT) topology with lightweight flow control deliver low LUT costs and features such as in-order delivery and livelock freedom. BFT NoCs make it possible to conFigure network bandwidth to match application requirements, by choosing switch types with different numbers of ports (arity) for the layers of the tree hierarchy. We increase the design space of BFT NoC configurations available to designers by constructing networks with larger arity-4 switches, in addition to the arity2 switches explored by previous works. When synthesized for the Xilinx UltraScale + VU9P FPGA, our proposed BFT NoCs consume 38-45% fewer LUTs and 33-50% smaller wiring lengths than arity-2 BFT NoCs with the same Rent parameter, in exchange for a reduction in maximum clock frequency in up to 25%. We simulate the operation of our proposed NoCs when routing various real-world workloads with 64 network clients, and show that they consistently achieve better Throughput / LUT cost ratios, when compared to arity-2 BFT NoCs with the same Rent parameter, with improvements of 15 to 120% depending on the benchmark and NoC topology.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".