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Record W3041919703 · doi:10.1109/lca.2020.3008288

MCsim: An Extensible DRAM Memory Controller Simulator

2020· article· en· W3041919703 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Computer Architecture Letters · 2020
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsMcMaster UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsComputer scienceExtensibilityDramEmbedded systemComputer architecture simulatorMemory controllerCAS latencyScheduling (production processes)Interface (matter)Computer architectureOperating systemComputer hardware

Abstract

fetched live from OpenAlex

Numerous proposals for memory controller (MC) designs have been exposed to the research community. Interest has since been growing in the area of computer architecture and real-time systems to improve the throughput of the system and/or guarantee timing requirements through novel scheduling algorithms. Consequently, comprehensive simulators are highly demanded since they provide an infrastructure for development of new ideas effectively without re-implementing the other parts of the hardware. Although there has been several proposals for off-chip memory device simulators, there is a shortage in their MC counterparts. In this letter, we propose MCsim, an extensible and cycle-accurate MC simulator. Designed as an integrable environment, MCsim is able to run as a trace-based simulator as well as provide an interface to connect with external CPU and memory device simulators.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.536
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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