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Record W1982662220 · doi:10.1080/00207210903168330

Aggressive drowsy cache cells

2010· article· en· W1982662220 on OpenAlexaff
Heba Shawkey, Dalia A. El-Dib, Z. Abid

Bibliographic record

VenueInternational Journal of Electronics · 2010
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsWestern University
Fundersnot available
KeywordsCacheBlock (permutation group theory)Computer scienceCPU cacheEmbedded systemParallel computing

Abstract

fetched live from OpenAlex

An aggressive drowsy cache block management, where the cache block is forced into drowsy mode all the time except during write and read operations, is proposed. The word line (WL) is used to enable the normal supply voltage (V DD_high) to the cache line only when it is accessed for read or write whereas the drowsy supply voltage (V DD_low) is enabled to the cache cell otherwise. The proposed block management neither needs extra cycles nor extra control signals to wake the drowsy cache cell, thereby reducing the performance penalty associated with traditional drowsy caches. In fact, the proposed aggressive drowsy mode can reduce the total power consumption of the traditional drowsy mode by 13% or even more, depending on the cache access rate, access frequency and the CMOS technology used.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.463

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 communication0.0000.000
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 head0.002
GPT teacher head0.203
Teacher spread0.201 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2010
Admission routes1
Has abstractyes

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