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. Intuitively, more blocks can be turned ON simultaneously if the blocks are constrained to have low current levels, and vice versa. In this paper, we propose a framework to manage this trade-off 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".