HLSCAM: Fine-Tuned HLS-Based Content Addressable Memory Implementation for Packet Processing on FPGA
Why this work is in the frame
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Bibliographic record
Abstract
Content Addressable Memories (CAMs) are pivotal in high-speed packet processing systems, enabling rapid data lookup operations essential for applications such as routing, switching, and network security. While traditional Register-Transfer Level (RTL) methodologies have been extensively used to implement CAM architectures on Field-Programmable Gate Arrays (FPGAs), they often involve complex, time-consuming design processes with limited flexibility. In this paper, we propose a novel templated High-Level Synthesis (HLS)-based approach for the design and implementation of CAM architectures such as Binary CAMs (BCAMs) and Ternary CAMs (TCAMs) optimized for data plane packet processing. Our HLS-based methodology leverages the parallel processing capabilities of FPGAs through employing various design parameters and optimization directives while significantly reducing development time and enhancing design portability. This paper also presents architectural design and optimization strategies to offer a fine-tuned CAM solution for networking-related arbitrary use cases. Experimental results demonstrate that HLSCAM achieves a high throughput, reaching up to 31.18 Gbps, 9.04 Gbps, and 33.04 Gbps in the 256×128, 512×36, and 1024×150 CAM sizes, making it a competitive solution for high-speed packet processing on FPGAs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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