Optimal partitioning of globally asychronous locally synchronous processor arrays
Bibliographic record
Abstract
With ongoing advances of semiconductor technology, power dissipation has been moving higher on the list of VLSI design constraints. In most high-performance synchronous VLSI designs, the distribution of low-skew global clock signals approaching GigaHertz range is the single largest source of power consumption. GALS design style offers a solution to this issue by dividing synchronous design into smaller locally synchronous sub-blocks. Smaller sub-blocks reduce capacitance in clock distribution networks because they need less H-tree levels. However, this implies a large number of sub-blocks, which increases the asynchronous power overhead. This work investigates these GALS power tradeoffs. This is, to our knowledge, the first paper to propose closed form models for optimum number of partitions that gives minimum power for a GALS array of identical processors. The models can serve as a useful firsthand guideline for designers in initial design stages. Experimental results verify the effectiveness of the model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".