A physical-level study of the compacted matrix instruction scheduler for dynamically-scheduled superscalar processors
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Bibliographic record
Abstract
This work studies physical-level characteristics of the recently proposed compacted matrix instruction scheduler for dynamically-scheduled, superscalar processors. Previous work focused on the matrix scheduler's architecture and argued in support of its speed and scalability advantages. However, no physical-level implementation or models were reported for it. Using full-custom layouts in a commercial 90 nm fabrication technology, this work investigates the latency and energy variations of the compacted matrix and its accompanying logic as a function of the issue width, the window size, and the number of global recovery checkpoints. This work also proposes an energy optimization that throttles unnecessary pre-charges and evaluations. This optimization reduces energy by 10% and 18% depending on the scheduler size.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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