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Record W2078111180 · doi:10.1109/isocc.2011.6138676

SRAM read-assist scheme for high performanc low power applications

2011· article· en· W2078111180 on OpenAlexaff
Ali Valaee, A.J. Al-Khalili

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsConcordia University
Fundersnot available
KeywordsStatic random-access memoryCMOSProcess variationComputer scienceReduction (mathematics)DissipationElectronic engineeringScheme (mathematics)Leakage powerPower (physics)Leakage (economics)Reliability (semiconductor)Access timeProcess (computing)Embedded systemComputer hardwareElectrical engineeringEngineeringTransistorVoltage

Abstract

fetched live from OpenAlex

In nanoscale CMOS technologies, SRAMs employ aggressively small cells, which makes them extremely vulnerable to process variation, degrading the worst case cell read current and threatening the reliability of sensing scheme. The increased effect of process variation in nanoscale technologies, along with continuous increase in the size of SRAMs, requires additional techniques and treatment such as read-assist techniques to ensure fast and reliable read operation. A read-assist circuit in 65nm CMOS technology is proposed in this paper which reduces the access time significantly and enhances SRAM cell stability. A complete comparison is made between the proposed scheme, conventional circuit and another state of the art design which shows speed improvement and power reduction of 55.3% and 21.3%, over conventional circuit respectively. Furthermore, in order to have the same sensing speed, the proposed scheme enables us to reduce cell V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DD</sub> by 227mV which results in considerable reduction in leakage power dissipation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.685
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.190
Teacher spread0.177 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations3
Published2011
Admission routes1
Has abstractyes

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