Statistical Thermal Profile Considering Process Variations: Analysis and Applications
Bibliographic record
Abstract
The nonuniform substrate thermal profile and process variations are two major concerns in the present-day ultra-deep submicrometer designs. To correctly predict performance/ leakage/reliability measures and address any yield losses during the early stages of design phases, it is desirable to have a reliable thermal estimation of the chip. However, the leakage power sources vary greatly due to process variations and temperature, which result in significant variations in the hotspot and thermal profile formation in very large scale integration chips. Traditionally, no leakage variations have been considered during full-chip thermal analysis. In this paper, the dependence behavior among the process variability, leakage power consumption, and thermal profile construction are established to effectively extract a reliable statistical thermal profile over a die at the microarchitectural level. Knowledge of this is the key to the design and analysis of circuits. The probability density functions of temperatures are extracted while considering the leakage variations due to the gate-length and oxide-thickness variations and while accounting for the coupling between the temperature and the total leakage. Two applications of the developed analyzer are investigated, namely, the evaluation of the hotspots' relocations and the total full-chip power estimation. Finally, the accuracy and efficiency of the developed analyzer are validated by comparisons with Monte Carlo simulations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".