Power-performance trade-offs for energy-efficient architectures: A quantitative study
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Bibliographic record
Abstract
The drastic increase in power consumption by modern processors emphasizes the need for power-performance trade-offs in architecture design space exploration and compiler optimizations. This paper reports a quantitative study on the power-performance trade-offs in software pipelined schedules for an Itanium-like EPIC architecture with dual-speed pipelines, in which functional units are partitioned into fast ones and slow ones. We have developed an integer linear programming formulation to capture the power-performance tradeoffs for software pipelined loops. The proposed integer linear programming formulation and its solution method have been implemented and tested on a set of SPEC2000 benchmarks. The results are compared with an Itanium-like architecture (baseline) in which there are four functional units (FUs) and all of them are fast units. Our quantitative study reveals that by introducing a few slow FUs in place of fast FUs in the baseline architecture, the total energy consumed by FUs can be considerably reduced. When 2 out of 4 FUs are set as slow, the total energy consumed by FUs is reduced by up to 31.1% (with an average reduction of 25.2%) compared with the baseline configuration, while the performance degradation caused by using slow FUs is small. If performance demand is less critical, then energy reduction of up to 40.3% compared with the baseline configuration can be achieved.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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