PERG: A scalable pattern-matching accelerator
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
PERG is an FPGA application for accelerating detection of computer virus signatures (patterns). A pattern consists of a sequence of one or more segments separated by gaps of fixed lengths. PERG preprocesses a database of these patterns into hardware. To our knowledge, PERG is the first pattern matching hardware targeting viruses, as well as the first among network intrusion detection systems (NIDS), which are similar in nature to PERG, to implement Bloomier filters. This makes guarding against false positives faster than traditional Bloom filters because verification requires checking against one pattern instead of several patterns. Using the ClamAV antivirus database, PERG fits 80,282 patterns containing over 8,224,848 characters into one modest FPGA chip with a small (4 MB) off-chip memory. The architecture achieves roughly 26x improved density (characters per memory bit) compared to the next-best NIDS pattern-matching engine which fits only 1/250 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> the characters. With an estimated throughput of about 200MB/s, PERG keeps up with most network or disk interfaces.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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