A new simple RC modeling for on-chip interconnects with its applications to buffer insertion
Bibliographic record
Abstract
A new improved RC modeling for on-chip interconnects derived from pi-configuration of AWE-Based RLC model is presented. A platform utilized to generate all-possible T- and pi configurations of RC, RLC and RLCG models using GAM, TPN, and AWE methods is proposed. 18 different RC, RLC, and RLCG models are generated based on this platform. The pi-configuration of AWE-RLC model provides the best performance. This model is mapped into an improved RC model to preserve the accuracy of the RLC model while keeping the simplicity of the RC model. As compared with conventional RC model, the simulation results of interconnect's delay with buffer insertion show that the proposed RC model improves the delay by 20.5%, reduces the number of required buffers by 24%, and the buffer sizes by 32%.
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How this classification was reachedexpand
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) | 0.000 | 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 | 0.001 |
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 teacher head, 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".