An FPGA Overlay Architecture for Cost Effective Regular Expression Search (Abstract Only)
Bibliographic record
Abstract
Snort and Bro are Deep Packet Inspection systems which express complex rules with regular expressions. Before performing a regular expression search, these applications apply a filter to select which regular expressions must be searched. One way to search a regular expression is through a Nondeterministic Finite Automaton (NFA). Traversing an NFA is very time consuming on a sequential machine like a CPU. One solution so is to implement the NFA into hardware. Since FPGAs are reconfigurable and are massively parallel they are a good solution. Moreover, with the advent of platforms combining FPGAs and CPUs, implementing accelerators into FPGA becomes very interesting. Even though FPGAs are reconfigurable, the reconfiguration time can be too long in some cases. This paper thus proposes an overlay architecture that can efficiently find matches for regular expressions. The architecture contains multiple contexts that allow fast reconfiguration. Based on the results of a string filter, a context is selected and regular expression search is performed. The proposed design can support all rules from a set such as Snort while significantly reducing compute resources and allowing fast context updates. An example architecture was implemented on a Xilinx® xc7a200 Artix-7. It achieves a throughput of 100 million characters per second, requires 20 ns for a context switch, and occupies 9% of the slices and 85% of the BRAM resources of the FPGA.
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.
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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".