High-Throughput Low-Energy Content-Addressable Memory Based on Self-Timed Overlapped Search Mechanism
Bibliographic record
Abstract
This paper introduces a self-timed overlapped search mechanism for high-throughput content-addressable memories (CAMs) with low search energy. Most mismatches can be found by searching the first few bits in a search word. Consequently, if a word circuit is divided into two sections that are sequentially searched, most match lines in the second section are unused. As searching the first section is faster than searching an entire word, we could potentially increase throughput by initiating a second-stage search on the unused match lines as soon as a first-stage search is complete. The overlapped search mechanism is realized using a self-timed word circuit that is independently controlled by a locally generated control signal, reducing the power dissipation of global clocking. A 256 x 144-bit CAM is designed under in 90 nm CMOS that operates with 5.57x faster throughput than a synchronous CAM, with 38% energy saving and 8% area overhead.
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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 | 0.000 |
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".