A low-power content-addressable memory (CAM) using pipelined hierarchical search scheme
Bibliographic record
Abstract
This paper presents two techniques to reduce power consumption in content-addressable memories (CAMs). The first technique is to pipeline the search operation by breaking the match-lines into several segments. Since most stored words fail to match in their first segments, the search operation is discontinued for subsequent segments, hence reducing power. The second technique is to broadcast small-swing search data on less capacitive global search-lines, and only amplify this signal to full swing on a shorter local search-line. As few match-line segments are active, few local search-lines will be enabled, again saving power. We have employed the proposed schemes in a 1024/spl times/144-bit ternary CAM in 1.8-V 0.18-/spl mu/m CMOS, illustrating an overall power reduction of 60% compared to a nonpipelined, nonhierarchical architecture. The ternary CAM achieves a 7-ns search cycle time at 2.89fJ/bit/search.
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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) | 0.003 | 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".