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
Packet processing systems increasingly need larger rulesets to satisfy the needs of deep-network intrusion prevention and cluster computing. FPGA-based implementations of packet processing systems have been proposed but their use of on-chip memory limits the number of rules these existing systems can maintain. Off-chip memories have traditionally been too slow to enable meaningful processing rates, but in this work we present a packet processing system that utilizes the much faster Hybrid Memory Cube (HMC) technology, enabling larger rulesets at usable line-rates. The proposed architecture streams rules from the HMC memory to a packet matching engine, using prefetching to hide the HMC access latency. The packet matching engine is replicated to process multiple packets in parallel. The final system, implemented on a Xilinx Kintex Ultrascale 060, processes 160 packets in parallel, achieving a 10~Gbps line-rate with approximately 1500 rules and a 16~Mbps line-rate with 1M rules. To the best of our knowledge, this is the first hardware solution capable of maintaining rulesets of this size. We present this work as an exploration of the application of HMCs to packet processing and as a first step in achieving a processing capability of a million rules at usable line-rates.
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