Improving DVFS in NoCs with Coherence Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As Networks-on-Chip (NoCs) continue to consume a large fraction of the total chip power budget, dynamic voltage and frequency scaling (DVFS) has evolved into an integral part of NoC designs. Efficient DVFS relies on accurate predictions of future network state. Most previous approaches are reactive and based on network-centric metrics, such as buffer occupation and channel utilization. However, we find that there is little correlation between those metrics and subsequent NoC traffic, which leads to suboptimal DVFS decisions. In this work, we propose to utilize highly predictable properties of cache-coherence communication to derive more specific and reliable NoC traffic predictions. A DVFS mechanism based on our traffic predictions, reduces power by 41% compared to a baseline without DVFS and by 21% on average when compared to a state-of-the-art DVFS implementation, while only degrading performance by 3%.
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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 | 0.000 |
| 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