MétaCan
Menu
Back to cohort
Record W2759700740 · doi:10.1109/mwscas.2017.8053199

A hybrid memristor-CMOS multiplier design based on memristive universal logic gates

2017· article· en· W2759700740 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMemristorCMOSLogic gatePass transistor logicLogic familyComputer scienceElectronic engineeringMultiplier (economics)Digital electronicsLogic synthesisVery-large-scale integrationElectronic circuitAND-OR-InvertMemistorComputer architectureElectrical engineeringEngineeringEmbedded systemResistive random-access memoryVoltage

Abstract

fetched live from OpenAlex

Memristor is considered as one of the promising solutions to the fundamental limitations of the VLSI systems. Logic implementation with memristor device by considering its compatibility with CMOS fabric provides a new vision for digital logic circuits. This work presents a 2 by 2 multiplier cell design using a hybrid CMOS-memristor universal gate. The universal gate based implementation approach is the extension for memristor ratioed logic (MRL) with lower implementation cost. Simulation results confirm functionality of the proposed circuit. This circuit requires 16 memristors, 8 transistors and only one computational time step for multiplication. Compared with previous works, this approach presents considerably lower implementation cost.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.662

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 head0.037
GPT teacher head0.249
Teacher spread0.212 · 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

Quick stats

Citations27
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Memory and Neural ComputingFrench-language works237,207