Generating Current Budgets to Guarantee Power Grid Safety
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Efficient and early verification of the chip power distribution network is a critical step in modern chip design. Vectorless verification, developed over the last decade as an alternative to simulation-based methods, requires user-specified current constraints (budgets) and checks if the corresponding worst-case voltage drops at all grid nodes are below user-specified thresholds. However, obtaining/specifying the current constraints remains a burdensome task for users. In this paper, we define and address the inverse problem: for a given grid, we would like to generate circuit current constraints which, if adhered to by the underlying logic, would guarantee grid safety. There are many potential applications for this approach, including various grid quality metrics, as well as voltage drop aware placement and floorplanning. We give a rigorous problem definition and develop some key theoretical results related to maximality of the current space defined by the constraints. Based on this, we then develop two algorithms for constraints generation that target the peak total chip power that is allowed by the grid and the uniformity of the temperature distribution. Finally, we develop a superior algorithm which targets a combination of both quality metrics.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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