Packet Matching on FPGAs Using HMC Memory
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.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".