Technology-driven limits on runtime power management algorithms for multiprocessor systems-on-chip
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Bibliographic record
Abstract
Runtime power management is a critical technique for reducing the energy footprint of digital electronic devices and enabling sustainable computing, since it allows electronic devices to dynamically adapt their power and energy consumption to meet performance requirements. In this article, we consider the case of MultiProcessor Systems-on-Chip (MPSoC) implemented using multiple Voltage and Frequency Islands (VFIs) relying on fine-grained Dynamic Voltage and Frequency Scaling (DVFS) to reduce the system power dissipation. In particular, we present a framework to theoretically analyze the impact of three important technology-driven constraints; (i) reliability-driven upper limits on the maximum supply voltage; (ii) inductive noise-driven constraints on the maximum rate of change of voltage/frequency; and (iii) the impact of manufacturing process variations on the performance of DVFS control for multiple VFI MPSoCs. The proposed analysis is general, in the sense that it is not bound to a specific DVFS control algorithm, but instead focuses on theoretically bounding the performance that any DVFS controller can possibly achieve. Our experimental results on real and synthetic benchmarks show that in the presence of reliability- and temperature-driven constraints on the maximum frequency and maximum frequency increment, any DVFS control algorithm will lose up to 87% performance in terms of the number of steps required to reach a reference steady state. In addition, increasing process variations can lead to up to 60% of fabricated chips being unable to meet the specified DVFS control specifications, irrespective of the DVFS algorithm used. Nonetheless, we note that although conventional DVFS might become less effective with technology scaling, it will continue to play an important role in the context of emerging power management techniques, for example, for massively parallel multiprocessor systems where only a subset of cores can be turned on at any given point of time due to total power constraints.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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