Managing Laser Power in Silicon-Photonic NoC Through Cache and NoC Reconfiguration
Why this work is in the frame
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Bibliographic record
Abstract
In manycore systems, the silicon-photonic link technology is projected to replace electrical link technology for global communication in network-on-chip (NoC) as it can provide as much as an order of magnitude higher bandwidth density and lower data-dependent power. However, a large amount of fixed power is dissipated in the laser sources required to drive these silicon-photonic links, which negates any bandwidth density advantages. This large laser power dissipation depends on the number of on-chip silicon-photonic links, the bandwidth of each link, and the photonic losses along each link. In this paper, we propose to reduce the laser power dissipation at runtime by dynamically activating/deactivating L2 cache banks and switching ON/OFF the corresponding silicon-photonic links in the NoC. This method effectively throttles the total on-chip NoC bandwidth at runtime according to the memory access features of the applications running on the manycore system. Full-system simulation utilizing Princeton application repository for shared-memory computers and Stanford parallel applications for shared-memory-2 parallel benchmarks reveal that our proposed technique achieves on an average 23.8% (peak value 74.3%) savings in laser power, and 9.2% (peak value 26.9%) lower energy-delay product for the whole system at the cost of 0.65% loss (peak value 2.6%) in instructions per cycle on average when compared to the cases where all L2 cache banks are always active.
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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