A Stateful Extension to P4THLS for Advanced Telemetry and Flow Control
Bibliographic record
Abstract
Programmable data planes are increasingly essential for enabling In-band Network Telemetry (INT), fine-grained monitoring, and congestion-aware packet processing. Although the P4 language provides a high-level abstraction to describe such behaviors, implementing them efficiently on FPGA-based platforms remains challenging due to hardware constraints and limited compiler support. Building on P4THLS framework, which leverages HLS for FPGA data-plane programmability, this paper extends the approach by introducing support for P4-style stateful objects and a structured metadata propagation mechanism throughout the processing pipeline. These extensions enrich pipeline logic with real-time context and flow-level state, thereby facilitating advanced applications while preserving programmability. The generated codebase remains extensible and customizable, allowing developers to adapt the design to various scenarios. We implement two representative use cases to demonstrate the effectiveness of the approach: an INT-enabled forwarding engine that embeds hop-by-hop telemetry into packets and a congestion-aware switch that dynamically adapts to queue conditions. Evaluation of an AMD Alveo U280 FPGA implementation reveals that incorporating INT support adds roughly 900 LUTs and 1000 Flip-Flops relative to the baseline switch. Furthermore, the proposed meter maintains rate measurement errors below 3% at 700 Mbps and achieves up to a 5× reduction in LUT and 2× reduction in Flip-Flop usage compared to existing FPGA-based stateful designs, substantially expanding the applicability of P4THLS for complex and performance-critical network functions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".