NoC<sup>2</sup>: An Efficient Interfacing Approach for Heavily-Communicating NoC-Based Systems
Bibliographic record
Abstract
Current research in interfacing clusters within Hierarchical Networks-on-Chip (HNoC) as well as interfacing NoC-based systems adopts a centralized approach. In this approach, a specific Processing Element (PE) acts as a gateway between interfacing peripherals and the rest of NoC elements. This article evaluates this approach and show that it is not optimal for handling the inter-NoC communication. Routing inter-NoC traffic through a system to its gateway PE deteriorates the network performance. Results show that both the throughput and latency of the centralized approach degrade with the increase in the inter-NoC traffic bandwidth. To alleviate this, we propose a novel distributed approach, which separates the inter-NoC traffic from the intra-NoC one. Our approach relies on distributed buffers to allow PEs to efficiently communicate with the interfacing peripheral. We evaluate our approach against other interfacing ones using synthetic traffic as well as real benchmark applications. Our evaluation covers the whole system performance as well as its inter- and intra-NoC parts. Results prove that the proposed approach outperforms previous interfacing ones in terms of throughput and latency. The proposed approach significantly enhances the inter-NoC performance without any deterioration in the intra-NoC one. Considering the inter-NoC performance, we achieve a throughput that is close to the maximum possibly attainable one. Other approaches show major performance degradation, reaching as low as 10% of this maximum attainable throughput.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".