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Record W2115861312 · doi:10.1109/iscas.1993.394263

Analog neural network building blocks based on current mode subthreshold operation

2002· article· en· W2115861312 on OpenAlexaff
Liyan Song, M.I. Elmasry, Anthony Vannelli

Bibliographic record

Venue1993 IEEE International Symposium on Circuits and Systems · 2002
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSubthreshold conductionArtificial neural networkComparatorComputer scienceElectronic engineeringCurrent (fluid)Analog signalAnalog signal processingAmplifierOperational amplifierElectronic circuitMultiplier (economics)Field-programmable analog arrayAnalogue electronicsFeed forwardElectrical engineeringAnalog multiplierTransistorArtificial intelligenceCMOSVoltageEngineeringComputer hardwareControl engineeringDigital signal processing

Abstract

fetched live from OpenAlex

A class of analog circuit building blocks for neural network implementation is presented which is based on the current mode subthreshold operation. This class of circuits includes current inverter, current-current multiplier, current amplifier and current comparator. A feedforward network with learning constructed by these building blocks was verified. These building blocks can be applied to artificial neural network (ANN) design in particular and to analog signal processing in general.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: none
Teacher disagreement score0.591
Threshold uncertainty score0.781

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 communication0.0010.000
Open science0.0010.000
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 head0.042
GPT teacher head0.280
Teacher spread0.238 · 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

Citations10
Published2002
Admission routes1
Has abstractyes

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