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Record W4380272255 · doi:10.23977/jeis.2023.080206

Hardware Implementation of Artificial Synapses

2023· article· en· W4380272255 on OpenAlex
Chen Liu

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Electronics and Information Science · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsnot available
Fundersnot available
KeywordsCrossbar switchComputer scienceLine (geometry)Path (computing)Array data structureLimitingComputer hardwarePerpendicularWord (group theory)Process (computing)Electrical engineeringElectronic engineeringTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

The simple two-terminal metal-insulator-metal (MIM) structures of memristors make them capable of being integrated into dense crossbar arrays [1, 2]. As shown in Figure 1, a typical crossbar array consists of parallel metal lines, termed word lines and bit lines, respectively, as the top and bottom electrodes that are perpendicular to each other. The bidirectional memory is connected between the word line and the bit line. During the read process, the extra current generated by the natural path represented by the red solid line can reduce the energy consumption of the unselected unit, thus reducing the read mosaic and limiting the size of the array. In order to independently control the read/write operations of each memory unit in the array, the 1T1R structure is proposed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.160

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 communication0.0000.002
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 head0.014
GPT teacher head0.291
Teacher spread0.277 · 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