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Record W2067184091 · doi:10.1049/ip-cds:20000237

Sensitivity study and improvements on a nonlinear resistive-type neuron circuit

2000· article· en· W2067184091 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEE Proceedings - Circuits Devices and Systems · 2000
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsNMOS logicPMOS logicResistorNonlinear systemVery-large-scale integrationCMOSComputer scienceSensitivity (control systems)Electronic engineeringInterconnectionMultiplier (economics)TransistorTopology (electrical circuits)Electrical engineeringEngineeringPhysicsVoltageTelecommunications

Abstract

fetched live from OpenAlex

A generalised VLSI circuit realisation for a nonlinear active resistor-type neuron is proposed that implements a saturating sigmoidal-like function by combining the nonlinear characteristics of NMOS and PMOS transistors. The circuit design is based on using a parameter sensitivity analysis to develop a robust design that will be relatively insensitive to process-parameter variations over the area of the die. The nonlinear resistor has been integrated into a module which realises a programmable digital synaptic weight capability. A neuron is effectively formed from the parallel interconnection that takes place as multiplier outputs are connected to create an input node to the resultant distributed neuron. Designs in 0.35 and 0.8 µm processes are compared with a conservative 1.2 µm CMOS implementation.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.786

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 science0.0000.000
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 head0.028
GPT teacher head0.253
Teacher spread0.225 · 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