Application-Driven Voltage-Island Partitioning for Low-Power System-on-Chip Design
Bibliographic record
Abstract
Among the different methods of reducing power for core-based system-on-chip (SoC) designs, thevoltage-islandtechniquehas gained in popularity. Assigning cores to the different supply voltages and floorplanning to create contiguous voltage islands are two important steps in the design process. We propose a new application-driven approach to voltage partitioning and island creation with the objective of reducing overall SoC power, area, and floorplanner runtime. Given an application power-state machine (PSM), we first identify the suitable range of supply voltages for each core. Then, we generate the discrete voltage assignment table using a heuristic technique. Next, we describe a methodology of reducing the large number of available choices from the voltage assignment table down to a useful set using the application PSM. We partition the cores into islands, using a cost function that gradually shifts from a power-based assignment to a connectivity-based one. Compared with previously reported techniques, a 9.4% reduction in power and 8.7% reduction in area are achieved using our approach, with an average runtime improvement of 2.4 times.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".