Bibliographic record
Abstract
Deflection-routed NoCs like Hoplite and HopliteRT take advantage of FPGA-specific features to deliver low-cost, high-frequency, FPGA-friendly communication networks. However, they suffer from long packet deflection penalties, low sustained throughputs, and feature limitations such as out-of-order delivery of packets. In this paper, we introduce the HopliteBuf NoC, and an associated static analysis tool, that eliminates deflections entirely while simultaneously adding in-order delivery feature using (1) small, stall-free FIFOs with provable occupancy bounds, and (2) linearization of vertical rings of the torus Hoplite topology to improve provable link utilization. We implement these FIFOs using cheap LUT SRAMs (Xilinx SRL32s, and Intel MLABs) to absorb packet contention. We evaluate conditions for stall-free behavior using static analysis that compute upper bounds on FIFO occupancy based on the communication pattern. Our static analysis deliver bounds that are not only better (in latency) than HopliteRT but also tighter by 2--3×. Across 100 randomly-generated flowsets mapped to a 5×5 system size, HopliteBuf is able to route a larger fraction of these flowsets with \textless 128-deep FIFOs, boost worst-case routing latency by $\approx$2× for mutually feasible flowsets. At 20% injection rates, HopliteRT is only able to route 1--2% of the flowsets while HopliteBuf can deliver 40--50% sustainability.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".