Comparative study and prediction modeling of photonic ring Network on Chip architectures
Bibliographic record
Abstract
Multicore systems are becoming state-of-the-art and therefore need fast and energy efficient interconnects to take full advantage of the computational capabilities. Integration of silicon photonics with traditional electrical interconnect in Network on Chip (NoC) proposes a promising solution for overcoming the scalability issues of electrical interconnect. In this paper, we implement the simulation model for two Optical NoC architectures and compare their performance. We also derive and evaluate a prediction modeling technique for the design space exploration of ONoCs. Our proposed model accurately predicts packet latency, static and dynamic energy consumption of the network. This work specifically addresses the challenge of accurately estimating performance metrics without having to incur high costs of exhaustive simulations. Our case study shows that by using only 10% of the entire design space, our proposed technique builds a prediction model that achieved average error rates as low as 5.44%, 2.67% and 3.24% for network packet latency, static and dynamic energy consumption respectively in six different benchmarks from Splash-2 benchmark suite.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".