A Low-Latency Memory-Efficient IPv6 Lookup Engine Implemented on FPGA Using High-Level Synthesis
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Bibliographic record
Abstract
The emergence of 5G networks and real-time applications across networks has a strong impact on the performance requirements of IP lookup engines. These engines must support not only high-bandwidth but also low-latency lookup operations. This paper presents the hardware architecture of a low-latency IPv6 lookup engine capable of supporting the bandwidth of current Ethernet links. The engine implements the SHIP lookup algorithm, which exploits prefix characteristics to build a compact and scalable data structure. The proposed hardware architecture leverages the characteristics of the data structure to support low-latency lookup operations, while making efficient use of memory. The architecture is described in C++, synthesized with a highlevel synthesis tool, then implemented on a Virtex-7 FPGA. Compared to the proposed IPv6 lookup architecture, other wellknown approaches use at least 87% more memory per prefix, while increasing the lookup latency by a factor of 2.3×.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it