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Record W2114978035 · doi:10.1109/tnano.2015.2414352

Towards Power Optimization and Implementation of Probabilistic Circuits Using Single-Electron Technology

2015· article· en· W2114978035 on OpenAlexafffund
Ran Xiao, Chunhong Chen

Bibliographic record

VenueIEEE Transactions on Nanotechnology · 2015
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProbabilistic logicCMOSComputer scienceElectronic circuitReliability (semiconductor)Electronic engineeringIntegrated circuitPower optimizationNanoelectronicsLow-power electronicsLogic gateEnergy consumptionReliability engineeringEngineeringPower (physics)Electrical engineeringPower consumptionAlgorithmNanotechnologyArtificial intelligenceMaterials science

Abstract

fetched live from OpenAlex

With continuous CMOS technology scaling toward its physical limits, there has been a growing demand for next-generation technologies with nanometer scale and novel design architectures. Single-electron (SE) technology is one of those candidates that can lead to high density and low power consumption for large-scale integration, at a cost of reduced reliability. Considering the fact that probabilistic circuits are able to realize fault-tolerant architectures, implementing probabilistic circuits with SE technology would be a natural solution for future electronic applications. In this paper, we first study the probabilistic behavior and implementation of SE logic, and show an exponential relation between logic gate's reliability and its energy consumption. A gate-level power optimization for probabilistic circuits is then proposed to minimize their power cost under given reliability constraints. Comparison with simulated annealing (SA) based method shows that the proposed approach can obtain promising results within a reasonably short time.

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 score0.245
Threshold uncertainty score0.563

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)0.0000.000

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 head0.023
GPT teacher head0.258
Teacher spread0.235 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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