POMR: a power-aware interconnect optimization methodology
Bibliographic record
Abstract
As VLSI technologies scale down, the average die size is expected to remain constant or to slightly increase with each generation. This results in an average increase in the global interconnect lengths. To mitigate their impact, buffer insertion has become the most widely used technique. However, unconstrained buffering is expected to require several hundreds of thousands of global interconnect buffers. This increased number of buffers is destined to adversely impact the chip power consumption. In this paper, an optimal power maze routing and buffer insertion/sizing problem for a two-pin net is formulated, as a shortest paths ranking problem. The pseudopolynomial time bound of the new formulation fits well within the context of the increased number of buffers. In fact, power savings as high as 25% for the 130-nm technology with a 10% sacrifice in delay is achieved. Furthermore, with the advent of dual threshold technologies, power sensitive applications can substantially benefit from adopting dual threshold buffers. Accordingly, the proposed problem formulation is extended to incorporate the selection of the buffer threshold voltage, where a twofold increase in power savings is observed. During the assessment of the impact of technology scaling using a set of MCNC Benchmarks, an average power saving as high as 35% with a 10% sacrifice in delay is observed. In addition, there is a 10% variation in the power savings when accounting for the process variations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".