HPQ: A High Capacity Hybrid Priority Queue Architecture for High-Speed Network Switches
Bibliographic record
Abstract
This paper presents a fast hybrid priority queue architecture usable in todays high-speed networking devices. This architecture can be used for scheduling and prioritizing packets in the network data plane. Due to increasing traffic, a high capacity priority queue, with constant latency and guaranteed performance is highly needed. In this work, an important goal is to reduce latency to best support the upcoming 5G wireless standards. The proposed hybrid priority queue architecture enables pipelined en/dequeue operations with O(1) time complexity. The proposed architecture is implemented in C++ and is synthesized with the Vivado High-Level Synthesis tool. The reported results show the feasibility of the proposed solutions and are compared across a range of priority queue depths and performance metrics with existing approaches. An implementation of the proposed architecture on a ZC706 FPGA board works at 60 MHz and supports links operating at 10 Gb/s, with a total capacity of ½ million packet tags spread over 512 independent queues.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".