Power management in multi-core processors using automatic dynamic pipeline stage unification
Bibliographic record
Abstract
In the recent years, the rapid development of microprocessors has raise up the demand for high-performance and fast processing computing systems capable of performing multiple tasks. Multi-core processors are increasingly advocated as a viable solution to achieve high performance, but under the constraints associated with power bounds. Maintaining the power consumption of processors at an acceptable level is still a challenge. For instance, the size of transistors is set to go down to as small as 22nm. When this size starts to decrease and go below 30nm, the sub-threshold leakage will become an issue since the current technique of dynamic voltage frequency scaling (DVFS) used to conserve energy will become less useful. The reason for this is that the transistor size will decrease and the absolute maximum voltage at which it can be operated will also decrease, but the lower limit voltage will remain the same at 2.3Vth where Vth is threshold voltage. Thereby, there is a clear demand for alternatives for managing the energy consumption in chip multi-processors (CMPs). In this paper, a variable stage pipelining (VSP) or pipeline stage unification (PSU) is investigated as a potential successor to the DVFS technique. Theoretical results are provided, showing that our dynamic pipeline stage unification approach can be efficient in terms of power consumption, chosen as performance metric.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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".