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Record W1991047274 · doi:10.1109/async.2012.25

High-Throughput Low-Energy Content-Addressable Memory Based on Self-Timed Overlapped Search Mechanism

2012· article· en· W1991047274 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Packet Processing and Optimization
Canadian institutionsUniversity of Waterloo
FundersJapan Society for the Promotion of ScienceUniversity of Tokyo
KeywordsComputer scienceOverhead (engineering)ThroughputWord (group theory)Content-addressable memoryEnergy (signal processing)DissipationComputer hardwareCMOSContent-addressable storageEmbedded systemElectronic engineeringArtificial intelligenceEngineeringOperating system

Abstract

fetched live from OpenAlex

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.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.837

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 integrity0.0000.000
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 head0.025
GPT teacher head0.231
Teacher spread0.206 · 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