Prediction Modeling for Application-Specific Communication Architecture Design of Optical NoC
Bibliographic record
Abstract
Multi-core systems-on-chip are becoming state-of-the-art. Therefore, there is a need for a fast and energy-efficient interconnect to take full advantage of the computational capabilities. Integration of silicon photonics with a traditional electrical interconnect in a Network-on-Chip (NoC) proposes a promising solution for overcoming the scalability issues of electrical interconnect. In this article, we derive and evaluate prediction modeling techniques for the design space exploration (DSE) of application-specific communication architectures for an Optical Network-on-Chip (ONoC). Our proposed model accurately predicts network packet latency, contention delay, and the static and dynamic energy consumption of the network. This work specifically addresses the challenge of accurately estimating performance metrics of the entire design space without having to perform time-consuming and computationally intensive exhaustive simulations. The proposed technique, based on machine learning (ML), can build accurate prediction models using only 10% to 50% (best case and worst case) of the entire design space. The accuracy, expressed as R 2 (Coefficient of Determination) is 0.99901, 0.99967, 0.99996, and 0.99999 for network packet latency, contention delay, static energy consumption, and dynamic energy consumption, respectively, in six different benchmarks from the Splash-2 benchmark suite, chosen among 6 different machine learning prediction models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".