Multi-Objective Optimization to Improve Both Thermal and Device Performance of a Nonuniformly Powered Micro-Architecture
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Bibliographic record
Abstract
Integration of different functional components such as level two (L2) cache memory, high-speed I/O interfaces, and memory controller has enhanced microprocessor performance. In this architecture, certain functional units on the microprocessor dissipate a significant fraction of the total power while other functional units dissipate little or no power. This highly nonuniform power distribution results in a large temperature gradient with localized hot spots that may have detrimental effects on computer performance, product reliability, and yield. Moving the functional units may reduce the junction temperature but can affect performance by a factor as much as 30%. In this paper, a multi-objective optimization is performed to minimize the junction temperature without significantly altering the computer performance. The analysis was performed for 90 nm Pentium IV Northwood architecture operating at 3 GHz clock speed. Each functional unit on the die has a specific role, so functional units with similar roles were grouped together. Thus, the actual Pentium IV die was divided into four groups (front end, execution cores, bus and L2, and out-of-order engine). Repositioning constraints were determined using circuit delay models of major functional units in a micro-architectural simulator. Thus, depending on the scenario, relocating functional units can result in virtually no performance loss (less than 2% is assumed to be minimal and is reported as 0%) to as much as 30% performance loss. From the results, the minimum and the maximum temperatures were 56.6°C and 62.2°C. This ΔT corresponds to thermal design power of 60.2 W. For microprocessors with higher thermal design power (115 W) and operating at higher clock speed, higher ΔT can be realized. Based on this paper’s analysis, the optimized scenario resulted in a junction temperature of 56.6°C at the cost of a 14% performance loss.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it