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Record W2979921829 · doi:10.1109/ccece.2019.8861762

Power-Management based on Reconfigurable Last-Cache level on Non-volatile Memories in Chip-Multi processors

2019· article· en· W2979921829 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceCacheEmbedded systemComputer architectureCache-only memory architectureStatic random-access memorySystem on a chipCPU cacheContext (archaeology)Power managementChipParallel computingCache coloringPower (physics)Computer hardware

Abstract

fetched live from OpenAlex

With technology scaling and increasing parallelism levels of new embedded applications, multi-cores in chip-multiprocessors (CMP) has been increased. In this context, power consumption acts a critical issue concern in future CMPs with restricted of battery lifetime. For future CMPs architecting, 3D stacking of Last Level Cache (LLC) has been recently introduced as a new methodology to combat the performance challenges of 2D integration. However, the 3D design of LLCs incurs more leakage power utilization compared to conventional cache architectures in 2Ds due to dense integration. We present in this work a power-efficient reconfigurable hybrid last level cache architecture for future CMPs. The proposed hybrid architecture SRAM memory is incorporated with STT-RAM technology by using the characteristics for both new and traditional technologies. The experimental results show that the designed method minimizes power consumption under multi-programmed and multithreaded applications.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.822
Threshold uncertainty score0.680

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 science0.0010.000
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 head0.022
GPT teacher head0.256
Teacher spread0.234 · 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