Automatic process migration of datapath hard IP libraries
Why this work is in the frame
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Bibliographic record
Abstract
While essential for high-performance circuit design, the custom nature of datapath components confines their use in only a few microprocessor companies. The reusability of datapath intellectual property (IP) libraries is largely limited by their dependence on process technology. Layout migration tools today, which are based on layout compaction developed decades ago, cannot cope with the challenges involved. In this paper, we present a comprehensive datapath IP development framework that can perform process migration by accommodating advanced circuit considerations, layout architecture and transistor sizing, in addition to design rule satisfaction. We demonstrate the effectiveness of the framework by migrating the Berkeley low power library, originally developed for 1.2um MOSIS process, into TSMC 0.25um and 0.18um technology.
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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 | 0.001 |
| 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