Bibliographic record
Abstract
Binary Content Addressable Memories (BCAMs), also known as associative memories, are hardware-based search engines. BCAMs employ a massively parallel exhaustive search of the entire memory space, and are capable of matching a specific data within a single cycle. Networking, memory management, pattern matching, data compression, DSP, and other applications utilize CAMs as single-cycle associative search accelerators. 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, efficient and modular technique for constructing BCAMs out of standard SRAM blocks in FPGAs is proposed. Hierarchical search is employed to achieve high storage efficiency. Previous hierarchical search approaches cannot be cascaded since they provide a single matching address, this incurs an exponential increase of RAM consumption as pattern width increases. Our approach, however, efficiently regenerates a match indicator for every single address by storing indirect indices for address match indicators. Hence, the proposed method can be cascaded and exponential growth is alleviated into linear. Our method exhibits high storage efficiency and is capable of implementing up to 9 times wider BCAMs compared to other approaches. A fully parameterized Verilog implementation is being released as an open source library. The library has been extensively tested using Altera's Quartus and Model Sim.
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.
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) | 0.003 | 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".