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Record W1989519810 · doi:10.1145/1165573.1165635

A low power SRAM architecture based on segmented virtual grounding

2006· article· en· W1989519810 on OpenAlexaff
Mohammad Sharifkhani, Manoj Sachdev

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStatic random-access memoryComputer scienceLow-power electronicsCMOSDynamic demandLeakage (economics)Reduction (mathematics)TransistorEnergy consumptionThreshold voltageVoltageLow voltageEmbedded systemElectronic engineeringPower (physics)Electrical engineeringComputer hardwarePower consumptionEngineering

Abstract

fetched live from OpenAlex

A novel architecture for the reduction of both dynamic and static power consumption of static random access memories (SRAM) is presented. The scheme is based on the segmented virtual grounding (SVGND) of the SRAM cells. Substantial leakage reduction is achieved by increasing the threshold voltage of the cell transistors through body effect. The write and read energy consumptions are reduced significantly by decreasing the bitline voltage swing and the number of bitlines affected in each transaction. Unlike recently reported low-power schemes, SVGND allows multiple words to be placed in each row while keeping the dynamic power low. This feature is achieved by introducing an additional operation mode to the SRAM cells. The architecture is implemented in a 130nm CMOS technology. Using this scheme, the read and write array energy consumption can be saved by 44% and 84% respectively. Measurement results portraits 15 times leakage reduction compared to the conventional scheme.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.950

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)0.0010.000

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 head0.003
GPT teacher head0.168
Teacher spread0.165 · 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 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

Citations5
Published2006
Admission routes1
Has abstractyes

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