Design of a Low Latency 40 Gb/s Flow-Based Traffic Manager Using High-Level Synthesis
Bibliographic record
Abstract
This paper presents a traffic manager architecture targeting to meet today's networking requirements, especially reduced latency, and to support the upcoming 5G technology in the software defined networking context. The proposed traffic manager functionalities are policing, scheduling, shaping, and queuing of incoming traffic (packets). The incoming traffic is assumed to be a set of flows in a network processing unit. Traffic management imposes constraints on packets to be sent out in such a way to meet the allowed bandwidth quotas for each flow, and enforce desired quality of service (QoS) targets. The FPGA prototyped architecture is based on the C++ language and is synthesized with the Vivado High-Level Synthesis (HLS) tool. The proposed traffic manager design supports 40 Gb/s per egress port for 64-byte sized packets, running at 80 MHz when implemented on a ZC706 Xilinx board. A throughput improvement of 4.0× over previous reported works is claimed.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".