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Record W2041333130 · doi:10.1109/tcsii.2015.2407711

A Variation-Tolerant MRAM-Backed-SRAM Cell for a Nonvolatile Dynamically Reconfigurable FPGA

2015· article· en· W2041333130 on OpenAlexafffund
Aynaz Vatankhahghadim, William J. Song, Ali Sheikholeslami

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsMagnetoresistive random-access memoryField-programmable gate arrayStatic random-access memoryNon-volatile memoryComputer scienceSpin-transfer torqueRacetrack memoryUniversal memoryRandom accessEmbedded systemComputer hardwareRandom access memorySemiconductor memoryMemory managementPhysicsInterleaved memoryMagnetic fieldMagnetization

Abstract

fetched live from OpenAlex

Adding a spin-transfer-torque (STT) magnetoresistive random-access memory (MRAM) to a static random-access memory (SRAM) cell to produce an MRAM-backed SRAM cell for a nonvolatile field-programmable gate array (FPGA) is proposed. The proposed cell reduces the time to reconfigure the FPGA following a power-down and enables fast wake-ups and power gating. With the proposed restore operation, data are recalled with no error even in the presence of mismatch. Simulation results confirm that data can be stored in the proposed cell in 80 ns and restored in less than 1 ns.

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 score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.229
Teacher spread0.201 · 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

Citations27
Published2015
Admission routes2
Has abstractyes

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