DSCAM+: Latency-Guaranteed FPGA-Based Content Addressable Memory for SDN-Enabled Forwarding Plane
Bibliographic record
Abstract
This paper presents a new approach for implementing high-capacity content-addressable memories on field programmable gate arrays (FPGAs). This approach introduces a novel configurable hardware architecture complemented by a multi-objective heuristic optimization algorithm. This algorithm explores the design space and identifies the near-optimal configuration parameter values for a given search table content. In this approach, the matching operation is carried out partially using synthesized circuits within the FPGA logic fabric and partly relies on an SRAM-based bitmapping technique. The balance between logic and memory resource utilization can be adjusted to accommodate constraints and design priorities. The approach supports large search tables, offers high throughput and short-latency searches, and can be reconfigured to adapt to new matching rules. This adaptability makes it particularly well-suited for IP address lookup in SDN-enabled data planes. Experimental results demonstrate the effectiveness of this method. It enables the implementation of an IPv4 forwarding table with more than 520,000 prefixes on a cost-effective AMD-Xilinx UltraScale+ FPGA. This implementation delivers a lookup latency of under 26 ns and a throughput of over 235 million lookups per second. The source code for this work is accessible on GitHub.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".