Power Scheduling With Active <inline-formula> <tex-math notation="LaTeX">$RC$ </tex-math> </inline-formula> Power Grids
Bibliographic record
Abstract
Power gating is widely used in large chip design as a way to manage the total power dissipation and avoid overheating. It works by turning OFF the power supply to circuit blocks that are not required to operate in certain operational modes. Many authors have studied the scheduling of chip workload to manage total power and temperature. But power gating also has an impact on the supply voltage levels across the die, because voltage drop is generated in the grid depending on the combination of blocks that are ON. We consider the question of how to manage the chip workload so that supply voltage variations remain within specs. The worst case voltage drop is the result of two things: the power budgets that were allocated to the various circuit blocks during the design process and the combination of blocks that are turned ON in a given operational mode. In this paper, we propose a framework to manage this tradeoff between how many blocks are ON simultaneously and how big the power budgets of the individual blocks are, assuming resistive and capacitive (RC) elements in the power grid model. Subject to user guidance, we generate block-level circuit current constraints as well as an implicit binary decision diagram (BDD) that helps identify the safe working modes. If the blocks are designed to respect these constraints, then the BDD can be used during normal operation to check whether a candidate working mode is safe or not.
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How this classification was reachedexpand
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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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; both teacher heads agree on what is shown here.
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".