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Record W2759804946 · doi:10.1109/nanoarch.2017.8053709

Design and operational assessment of an intra-cell hybrid L2 cache

2017· article· en· W2759804946 on OpenAlexaff
Linbin Chen, Jie Han, Weiqiang Liu, Fabrizio Lombardi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCacheComputer scienceMacrocellDramStatic random-access memoryPhase-change memoryCPU cacheCache pollutionEmbedded systemCache coloringCache algorithmsComputer architectureParallel computingComputer hardwareOperating systemEngineering

Abstract

fetched live from OpenAlex

This paper deals with the integration of several emerging technologies (embedded DRAM (eDRAM) and Phase Change Memory (PCM)) with SRAM to leverage their operational features and achieve a hybrid L2 cache for improvements in density and power consumption. A novel hybrid cache replacement and migration policy is proposed; a hybrid macrocell is also designed using a different number of eDRAM, PCM and SRAM cells (referred to as the memory ratio). Two cache models at architectural and circuit levels are presented for intra-cell operation. A 4-way hybrid cache is considered throughout this manuscript as example. By simulating applications benchmarks, comparison and analysis on different replacement and migration policies and the impact of the memory ratio are provided to assess the proposed hybrid cache design.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.700
Threshold uncertainty score0.258

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 head0.031
GPT teacher head0.309
Teacher spread0.279 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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