Three-dimensional multiprocessor system-on-chip thermal optimization
Bibliographic record
Abstract
3D stacked wafer integration has the potential to improve multiprocessor system-on-chip (MPSoC) integration density, performance, and power efficiency. However, the power density of 3D MPSoCs increases with the number of active layers, resulting in high chip temperatures. This can reduce system reliability, reduce performance, and increase cooling cost. Thermal optimization for 3D MPSoCs imposes numerous challenges. It is difficult to manage assignment and scheduling of heterogeneous workloads to maintain thermal safety. In addition, the thermal characteristics of 3D MPSoCs differ from those of 2D MPSoCs because each stacked layer has a different thermal resistance to the ambient and vertically-adjacent processors have strong temperature correlation. We propose a 3D MPSoC thermal optimization algorithm that conducts task assignment, scheduling, and voltage scaling. A power balancing algorithm is initially used to distribute tasks among cores and active layers. Detailed thermal analysis is used to guide a hotspot mitigation algorithm that incrementally reduces the peak MPSoC temperature by appropriately adjusting task execution times and voltage levels. The proposed algorithm considers leakage power consumption and adapts to inter-layer thermal heterogeneity. Performance evaluation on a set of multiprogrammed and multithreaded benchmarks indicates that the proposed techniques can optimize 3DMPSoC power consumption, power profile, and chip peak temperature.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".