PrismParser: A Framework for Implementing Efficient P4-Programmable Packet Parsers on FPGA
Why this work is in the frame
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Bibliographic record
Abstract
The increasing complexity of modern networks and their evolving needs demand flexible, high-performance packet processing solutions. The P4 language excels in specifying packet processing in software-defined networks (SDNs). Field-programmable gate arrays (FPGAs) are ideal for P4-based packet parsers due to their reconfigurability and ability to handle data transmitted at high speed. This paper introduces three FPGA-based P4-programmable packet parsing architectural designs that translate P4 specifications into adaptable hardware implementations called base, overlay, and pipeline, each optimized for different packet parsing performance. As modern network infrastructures evolve, the need for multi-tenant environments becomes increasingly critical. Multi-tenancy allows multiple independent users or organizations to share the same physical network resources while maintaining isolation and customized configurations. The rise of 5G and cloud computing has accelerated the demand for network slicing and virtualization technologies, enabling efficient resource allocation and management for multiple tenants. By leveraging P4-programmable packet parsers on FPGAs, our framework addresses these challenges by providing flexible and scalable solutions for multi-tenant network environments. The base parser offers a simple design for essential packet parsing, using minimal resources for high-speed processing. The overlay parser extends the base design for parallel processing, supporting various bus sizes and throughputs. The pipeline parser boosts throughput by segmenting parsing into multiple stages. The efficiency of the proposed approaches is evaluated through detailed resource consumption metrics measured on an Alveo U280 board, demonstrating throughputs of 15.2 Gb/s for the base design, 15.2 Gb/s to 64.42 Gb/s for the overlay design, and up to 282 Gb/s for the pipelined design. These results demonstrate a range of high performances across varying throughput requirements. The proposed approach utilizes a system that ensures low latency and high throughput that yields streaming packet parsers directly from P4 programs, supporting parsing graphs with up to seven transitioning nodes and four connections between nodes. The functionality of the parsers was tested on enterprise networks, a firewall, and a 5G Access Gateway Function graph.
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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 | 0.000 |
| 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) | 0.000 | 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