An FPGA Coarse Grained Intermediate Fabric for Regular Expression Search
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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