An FPGA Coarse Grained Intermediate Fabric for Regular Expression Search
Bibliographic record
Abstract
Deep Packet Inspection systems such as Snort and Bro express complex rules with regular expressions. In Snort, the search of a regular expression is performed with a Non-deterministic Finite Automaton (NFA). Traversing an NFA sequentially with a CPU is not deterministic in time, and it can be very time consuming. The sequential traversal of an NFA with a CPU is not deterministic in time consequently it can be time consuming. A fully parallel NFA implemented in hardware can search all rules, but most of the time only a small part is active. Furthermore, a string filter determines the traversal of an NFA. This paper proposes an FPGA Intermediate Fabric that can efficiently search regular expressions. The architecture is configured for a specific NFA based on a partial match of a rule found by the string filter. It can thus support all rules from a set such as Snort, while significantly reduce compute resources and power con-sumption compared to a fully parallel implementation. Multiple parameters can be selected to find the best tradeoff between resource consumption and the number and types of supported expressions. This architecture was implemented on a Xilinx R XC7VX1140 Virtex-7. The reported implementation, can sustain up to 512 regular expressions, while requiring 2% of the slices and 16% of the BRAM resources, for a throughput of 200 million characters per second.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".