Efficient Power Consumption using Hybrid Emerging Memory Technology for 3D CMPs
Bibliographic record
Abstract
The development of computing systems, data analytics, and storages for big data computing has resulted in an increasing need for low-power computational platforms and high-performance efficiency, capable of adjusting the processing capability and storage domains. In this context, high performance acts as a critical issue in future CMPs with restricted of battery lifetime and power consumption. 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. We propose an uncore hybrid LLC which takes advantage of emerging memory technologies. In the former phase, a reconfiguration unit premised on a simple convex formulation is used to forecast the running application's bandwidth and selects a high-performance configuration aimed at the LLC. The experimental results showed a reduction in (average memory access time) AMAT 12% as compared to SRAM- cache for PARSEC benchmarks which lead to 88% minimization in power consumption on average.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".