PERG: A scalable FPGA-based pattern-matching engine with consolidated Bloomier filters
Bibliographic record
Abstract
PERG is an FPGA application that accelerates the process of searching a stream of bytes against a large, fixed database of string patterns. The stream could be network, disk, or file traffic, while the pattern database may represent computer viruses, spam, keyword sequences, or watermarks. A full pattern, or rule, consists of a sequence of one or more segments separated by gaps. Each segment is an exact sequence of bytes, possibly 100s of bytes long. Each gap contains arbitrary bytes, but is a known length. PERG uses a pattern compiler to transform a database of these rules into a hardware implementation. To the authorspsila knowledge, this is the first pattern match engine hardware designed for large virus databases. It is also first among network intrusion detection systems (NIDS), which are similar in nature to PERG, to implement Bloomier filters. Like hash tables, Bloomier filters produce false positives due to aliasing, so all potential matches must be verified by exact matching. However, Bloomier filters are more powerful than their ancestral Bloom filters because they can identify the exact rule of a potential match. This enables two key advantages for PERG. First, it allows PERG to use a checksum to very efficiently reduce false positives. Second, exact matching with PERG filters is much faster than with Bloom filter systems because only one suspect pattern needs to be checked, not all patterns. To reduce memory requirements, PERG packs as many segments as possible into each Bloomier filter by consolidating several different segment lengths into the same filter unit. This is done by dividing each segment into two smaller but overlapping fragments of the same length. Dividing into non-overlapping fragments would create shorter fragments of uneven lengths, leading to higher false positives and differing lengths to consolidate later. Using the ClamAV antivirus database, PERG fits 80,282 patterns containing over 8,224,848 characters into a single modest FPGA chip with a small (4 MB) off-chip memory. It uses just 26 filter units, resulting in roughly 26x improved density (characters per memory bit) compared to the next-best NIDS pattern match engine which fits only 1/250ththe characters. PERG can scan at roughly 200 MB/s and match the speed of most network or disk interfaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".