Managing Laser Power in Silicon-Photonic NoC Through Cache and NoC Reconfiguration
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".