Enhancing Butterfly Fat Tree NoCs for FPGAs with Lightweight Flow Control
Bibliographic record
Abstract
FPGA overlay networks-on-chip (NoCs) based on Butterfly Fat Tree (BFT) topology and lightweight flow control can outperform state-of-the-art FPGA NoCs, such as Hoplite and others, on metrics such as throughput, latency, cost and power efficiency, and features such as in-order delivery and bounded packet delivery times. On one hand, lightweight FPGA NoCs built on the principle of bufferless deflection routing, such as Hoplite, can deliver low-LUT-cost implementations but sacrifice crucial features such as in-order delivery, livelock freedom, and bounds on delivery times. On the other hand, capable conventional NoCs like CONNECT provide these features but are significantly more expensive in LUT cost. Butterfly Fat Trees with lightweight flow control can deliver these features at medium cost while providing bandwidth configuration flexibility to the developer. We design FPGA-friendly routers with (1) latency-insensitive interfaces, coupled with (2) deterministic routing policy, and (3) round-robin scheduling at NoC ports to develop switches that take 311-375 LUTs/router. We evaluate our NoC under various conditions including synthetic and real-world workloads to deliver resource-proportional throughput and latency wins over competing NoCs, while significantly improving dynamic power consumption when compared to deflection-routed NoCs. We also explore the bandwidth customizability of the BFT organization to identify best NoC configurations for resource-constrained and application-requirement constrained scenarios. We also evaluate hard implementations of these routers using TSMC 65nm standard cell technology and observe that 128b BFT t and pi switches fit in 123x122μ and 147x147μ tile sizes while operating at 1GHz.
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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) | 0.000 | 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) | 0.001 | 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".