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Record W2105664623 · doi:10.1109/ccece.2004.1345213

A static power reduction technique for ternary content addressable memories

2004· article· en· W2105664623 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
Fundersnot available
KeywordsStatic random-access memoryPower (physics)Reduction (mathematics)Computer scienceTransistorContent-addressable memoryElectronic circuitScalingLow-power electronicsDynamic demandPower consumptionElectrical engineeringVoltageComputer hardwarePhysicsEngineeringArtificial neural network

Abstract

fetched live from OpenAlex

Ternary content addressable memories (TCAMs) are attractive for high-speed packet forwarding and classification in network switches and routers. Traditionally, the static power in TCAMs has been a small fraction of the total power due to high activity of TCAMs. However, technology scaling and architecture level techniques are reducing the dynamic power of TCAMs. The technology scaling is also increasing the off-current of transistors. Hence, the static power is becoming a significant portion of the total power consumption in TCAMs. This paper presents a technique to reduce the static power in SRAM-based TCAMs without affecting the speed of operation. We analyze the circuits and present the trade-offs of using this power reduction technique. The simulation results show a significant reduction in the static-power (up to a factor of 11) for an SRAM-based TCAM in 0.13 /spl mu/m technology.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.545
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.032
GPT teacher head0.260
Teacher spread0.227 · 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