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Record W1934231135 · doi:10.1109/isqed.2004.1283728

An asymmetric SRAM cell to lower gate leakage

2004· article· en· W1934231135 on OpenAlexaff
Navid Azizi, Farid N. Najm

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLeakage (economics)Static random-access memorySubthreshold conductionCacheMaterials scienceOptoelectronicsComputer scienceElectronic engineeringParallel computingTransistorElectrical engineeringEngineeringComputer hardwareVoltage

Abstract

fetched live from OpenAlex

We introduce a new Static Random Access Memory (SRAM) cell that offers high stability and reduces gate leakage power in caches while maintaining low access latency. Our design exploits the strong bias towards zero at the bit level exhibited by the memory value stream of ordinary programs. Compared to conventional symmetric high-performance cell, our new cell reduces total leakage by more than 24% in the zero state at high temperature. With one cell design, total cache leakage is reduced by 24% at high temperature with no performance or stability loss. At low temperatures, where gate leakage is dominant, our cell reduces total cache leakage by 43%. We show that the new cell can be combined in an orthogonal fashion with asymmetric dual-V/sub t/ cells to lower both gate and subthreshold leakage, reducing total leakage by 45% to 60% with comparable performance and stability.

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 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 score0.065
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)0.0000.001

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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.

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

Citations35
Published2004
Admission routes1
Has abstractyes

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