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Record W2043493229 · doi:10.1109/jssc.2004.831433

A low-power content-addressable memory (CAM) using pipelined hierarchical search scheme

2004· article· en· W2043493229 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Journal of Solid-State Circuits · 2004
Typearticle
Languageen
FieldComputer Science
TopicNetwork Packet Processing and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPipeline (software)Computer sciencePower (physics)Content-addressable memorySwingLine (geometry)Content-addressable storageReduction (mathematics)Binary search algorithmComputer hardwareSearch algorithmReal-time computingArtificial intelligenceAlgorithmEngineeringMathematics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.594
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.295
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it