P* Admissible Thermal-Aware Matrix Floorplanner for 3D ICs
Bibliographic record
Abstract
Over the past decade, three-dimensional (3D) integrated circuits (ICs) have matured as a promising solution for high-performance computing systems. Achieving high power density (>1 W/mm2), however, poses a critical thermal management challenge, as heat is prone to be trapped within the layers of the 3D structure. Early-stage power-aware floorplanning is crucial to address thermal challenges, supporting the evaluation of multiple design alternatives. In this paper, a P* admissible thermally aware floorplanning algorithm for 3D ICs is proposed. The operation principle of the algorithm is based on storing and manipulating the data of the functional blocks in a matrix form, supporting polynomial optimization and packing time. Simulation results on standard benchmarks (MCNC and GSRC) exhibit a significant improvement in key performance metrics. A reduction in area, temperature, and runtime of up to, respectively, 10.1%, 17.7%, and 89.6% are observed, as compared to the state-of-the-art.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".