HopliteRT: An efficient FPGA NoC for real-time applications
Bibliographic record
Abstract
Overlay NoCs, such as Hoplite, are cheap to implement on an FPGA but provide no bounds on worst-case routing latency of packets traversing the NoC due to deflection routing. In this paper, we show how to adapt Hoplite to enable calculation of precise upper bounds on routing latency by modifying the routing function to prioritize deflections, and by regulating the injection of packets to meet certain throughput and burstiness constraints. We provide an analytical model for computing end-to-end latency in the form of (1) in-flight time in the network Tf, and (2) waiting time at the source node Ts. To bound in-flight time in an m × m NoC, we modify the routing function and switching crossbar richness in the Hoplite router to deliver Tf= ΔΧ + ΔΥ + (AY × m) + 2 where AX and ΔΥ are differences of the source and destination address co-ordinates of the packet. To bound the waiting time at the source, we add a Token Bucket regulator with rate ρiand burstiness σifor each flow fiof node (x, y) to deliver (⌈1/ρi⌉ − 1) + Ts: Ts= ⌈σ(ΓCf)/1−σ(ΓCf);⌉ which depends on the regulator period 1/ρi, burstiness a and the rate ρ of all interfering flows ΓCf. A 64b implementation of our HopliteRT router requires ≈4% fewer LUTs, and similar number of FFs compared to the original Hoplite router. We also need two small counters at each client port for regulating injection. We evaluate our model and RTL implementation across synthetic traffic patterns and observe behavior that conforms with the analytical bounds.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".