Power-Management based on Reconfigurable Last-Cache level on Non-volatile Memories in Chip-Multi processors
Bibliographic record
Abstract
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.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".