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Record W2105880860 · doi:10.1109/glsv.1998.665192

A low-power high-performance embedded SRAM macrocell

2002· article· en· W2105880860 on OpenAlexaff
Amr Fahim, Muhammad Khellah, M.I. Elmasry

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStatic random-access memoryMacrocellLow-power electronicsCMOSDecoding methodsComputer scienceDissipationPower (physics)Low voltageApplication-specific integrated circuitVoltageElectronic engineeringEmbedded systemComputer hardwareElectrical engineeringEngineeringPhysicsPower consumptionTelecommunications

Abstract

fetched live from OpenAlex

A new approach to modeling the decoding hierarchy in a hierarchical word line (HWL) SRAM architecture using integer-linear programming (ILP) is introduced. Using this approach, the HWL architecture is shown to be inadequate for very large SRAM sizes. Alternatively, a new low-power high-speed SRAM architecture is described. This architecture is shown to have fairly constant speed and power dissipation for sizes ranging between 32 kb to 4 Mb. Low-power is achieved by a voltage boosting technique not requiring a two-step voltage and by a new method of tristating memory cells during a write operation. The SRAM was implemented in a 0.35 /spl mu/m CMOS technology operated at 150 MHz while dissipating only 10 mW.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.010

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 head0.006
GPT teacher head0.165
Teacher spread0.158 · 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 designSimulation or modeling
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
Published2002
Admission routes1
Has abstractyes

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