Unleashing the Power of FPGAs as Programmable Switches
Bibliographic record
Abstract
The P4 language and the PISA architecture have revolutionized the field of networking. Thanks to P4 and PISA, new networking applications and protocols can be rapidly evaluated on high performance switches. While P4 allows the expression of a wide range of packet processing algorithms, current programmable switch architecture limit the overall processing flexibility. To address this shortcoming recent work have proposed to implement PISA on FPGAs. However, little effort has been devoted to analyze whether FPGAs are good candidates to implement PISA. In this work, we take a step back and evaluate the micro-architecture efficiency of various PISA blocks. Using a theoretical analysis and experiments, we demonstrate that current FPGA architecture drastically limit the performance of a few PISA blocks. Thus, we explore two avenues to alleviate these shortcomings. First, we identify some network applications that are well tailored to current FPGAs. Second, to support a wider range of networking applications, we propose modifications to the FPGA architecture which can also be of interest outside the networking field.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".