Determining schedules for reducing power consumption using multiple supply voltages
Bibliographic record
Abstract
Dynamic power is the main source of power consumption in CMOS circuits. It depends on the square of the supply voltage. It may significantly be reduced by scaling down the supply voltage of some computational elements in the circuit, with the penalty of an increase of their execution delay. To reduce the dynamic power consumption, without degrading the performance determined assuming that the circuit operates at the highest available supply voltage, the supply voltage of computational elements off critical paths can be scaled down. Defined here as MinP/sub dyn/, the problem of minimizing the dynamic power consumption, under performance constraints, by scaling down the supply voltage of computational elements on non-critical paths is NP-hard in general. Solving MinP/sub dyn/ for multi-phase clocked sequential circuits may allow to reduce their power consumption and the required number of registers. Reducing the number of registers also allows to reduce the power consumption, the number of control signals, and the area of the circuit. In this paper, we focus on devising methods to efficiently solve MinP/sub dyn/ for designs modeled as cyclic or acyclic graphs. More precisely, once the circuit is optimized for timing constraints, then we look for schedules that allow the computational elements of the circuit to operate at the lowest possible supply voltage. We present an integer linear programming formulation for that problem, which we use to devise a polynomial time solvable method and an exact algorithm based on a branch-and-bound technique. Experimental results confirm the effectiveness of the method and power reduction factors as high as 53.84% were obtained. Also, they show that the exact algorithm produces optimal results in a small number of tries, which is due to the rules used to prune useless solutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".