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Record W2086373044 · doi:10.1109/tvlsi.2015.2411258

Logic-in-Memory With a Nonvolatile Programmable Metallization Cell

2015· article· en· W2086373044 on OpenAlexaff
Pilin Junsangsri, Jie Han, Fabrizio Lombardi

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPass transistor logicLogic familyLogic gateComputer scienceCMOSLogic levelTransistorProgrammable logic arrayProgrammable logic deviceMemory cellElectronic engineeringElectrical engineeringLogic synthesisVoltageEngineeringComputer hardwareAlgorithm

Abstract

fetched live from OpenAlex

This paper introduces two new cells for logic-in-memory (LiM) operation. The first novelty of these cells is the resistive random access memory configuration that utilizes a programmable metallization cell as nonvolatile element. CMOS transistors and ambipolar transistors are used as processing and control elements for the logic operations of the LiM cells. The first cell employs ambipolar transistors and CMOS in its logic circuit (7T2A1P), while the second LiM cell uses only MOSFETs (9T1P) to implement logic functions, such as AND, OR, and XOR. The operational mode of the proposed cells is voltage-based, which is much different from the previous designs in which a LiM cell operates on a current mode. Extensive simulation results using HSPICE are provided for the evaluation of these cells; comparison shows that the proposed two cells outperform previous LiM cells in metrics, such as logic operation delays, power delay product, circuit complexity, write time, and output swing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.227
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2015
Admission routes1
Has abstractyes

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