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Record W3108022219 · doi:10.1145/3400286.3418248

Overheating-Avoidance Remapping Scheme for Reliability Enhancement of 3D PCM Storage Systems

2020· article· en· W3108022219 on OpenAlexaff
Yu-Chen Lin, Tse-Yuan Wang, Che-Wei Tsao, Yuan-Hao Chang, Jian-Jia Chen, Xue Liu, Tei‐Wei Kuo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsOverheating (electricity)Phase-change memoryComputer scienceEmbedded systemComputer data storageComputer hardwarePhase changeEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

With the trend of 3D architecture and higher access rate, Phase Change Memory (PCM) storage devices face the overheating issue. This work is motivated by the observation that PCM devices might change states of memory cells with high temperature, and it will hurt the reliability of 3D PCM storage systems. Hence, we propose an Overheating-Avoidance Remapping Scheme (OARS) that controls the temperature of PCM layers and achieves wear-leveling of PCM cells inside PCM devices. Besides, we also take remapping overhead into consideration. The experiments were conducted based on the representative realistic workloads, and the results demonstrate the efficacy of the proposed scheme.

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 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: Methods · Consensus signal: Methods
Teacher disagreement score0.434
Threshold uncertainty score0.431

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 science0.0010.000
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 head0.028
GPT teacher head0.265
Teacher spread0.236 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2020
Admission routes1
Has abstractyes

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