Scalable memory-less architecture for string matching with FPGAs
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
String matching hardware engines generally utilize Ternary Content Addressable Memories (TCAMs). Although TCAM-based solutions are fast, they are expensive and power hungry. This paper proposes a high-performance memory-less architecture for string matching called Split-Bucket. It offers a performance comparable to TCAM-based solutions. Moreover, it is reconfigurable and scalable to the size of the target string set and the width of the string. The architecture is characterized using the Longest Prefix Match problem for IP address lookup and is implemented on a Virtex-7 FPGA. For a real-world routing table with 524 k IPv4 prefixes, the Split-Bucket architecture achieves a throughput of 103.4 M packets per second and consumes 23% and 22% of the Look Up Tables and Flip-Flops of a Xilinx XC7V2000T chip, respectively.
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