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Record W4378800877 · doi:10.1145/3583781.3590325

Statistical Weight Refresh System for CTT-Based Synaptic Arrays

2023· article· en· W4378800877 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 institutionsMcGill University
Fundersnot available
KeywordsNeuromorphic engineeringComputer scienceThreshold voltageTrap (plumbing)VoltageCapacitorElectrical engineeringTransistorPhysicsArtificial neural networkEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Charge-trap transistors (CTTs) are compute-in-memory devices that are used to model synaptic arrays in neuromorphic systems. CTTs enable non von Neumann architectures, thus, eliminating the energy spent on compute-memory communication. Synaptic weights can be stored in CTTs by shifting the threshold voltage of the devices in an analog manner. CTTs are, however, susceptible to unintentional de-trapping of charge over time due to threshold voltage instability, leading to loss of the stored synaptic weights. The proposed weight refresh system performs statistical refresh of the CTT array to replenish the charge of individual CTT devices (restore synaptic weights) based on characterization of threshold voltage instability in high-k dielectrics.

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 score0.931
Threshold uncertainty score0.354

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 head0.020
GPT teacher head0.250
Teacher spread0.230 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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