Statistical Peak Temperature Prediction and Thermal Yield Improvement for 3D Chip Multiprocessors
Bibliographic record
Abstract
Thermal issues have become critical roadblocks for achieving highly reliable three-dimensional (3D) integrated circuits (ICs). The presence of process variations further exacerbates these problems. In this article, we propose techniques for the efficient evaluation and mitigation of the impact of leakage power variations on the temperature profile of 3D Chip Multiprocessors (CMPs). Experimental results demonstrate that, due to the impact of process variations, a 4-tier 3D implementation can be more than 40ˆC hotter and 23% leakier than its 2D counterpart. To determine the maximum temperature of each fabricated 3D IC, we propose an accurate learning-based model for peak temperature prediction. Based on the learning model, we then propose two post-fabrication techniques to increase the thermal yield of 3D CMPs: (1) tier restacking and (2) thermally-aware die matching. Experimental results show that: (1) the proposed prediction model achieves more than 98% accuracy, and (2) the proposed thermally-aware, post-fabrication optimization techniques significantly improve the thermal yield from only 51% to 99% for 3D CMPs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".