Deep and narrow binary content-addressable memories using FPGA-based BRAMs
Bibliographic record
Abstract
Binary Content Addressable Memories (BCAMs) are massively parallel search engines capable of searching the entire memory space in a single clock cycle. BCAMs are used in a wide range of applications, such as memory management, networks, data compression, DSP, and databases. Due to the increasing amount of processed information, modern BCAM applications demand a deep searching space. However, traditional BCAM approaches in FPGAs suffer from storage inefficiency. In this paper, a novel and efficient technique for constructing deep and narrow BCAMs out of standard SRAM blocks in FPGAs is proposed. This technique is most efficient for deep and narrow CAMs since the BRAM consumption is exponential to pattern width. Using Altera's Stratix V device, traditional methods achieve up to 64K-entry BCAM while the proposed technique achieves up to 4M entries. For the 64K-entry test-case, traditional methods consume 43 times more ALMs and achieves only one-third of the Fmax. A fully parameterized Verilog implementation is available1. This implementation has been extensively tested using Altera's tools.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".