Performance evaluation of three Network-on-Chip (NoC) architectures (Invited)
Bibliographic record
Abstract
As the number of processing elements which can be placed on a single chip doubles about every two years, both System-on-Chip (SoC) and the microprocessor market call for high-performance, flexible, scalable, and design-friendly interconnection network architectures [1]. Network-on-Chip (NoC) has been proposed as a solution to multi-core communication problems. The advantages of NoC include high bandwidth, low latency, low power consumption and scalability. The interconnection architecture has a significant impact on the performance of networks in terms of point-to-point delay, throughput, and loss rate. We evaluate the performance of three NoC architectures, including the torus, the Metacube and the hypercube under Poisson and bit-complement traffic pattern. Network sizes of 32, 64, 128, 512 and 1024 nodes are considered. Three injection rates ranging from 10% to 30% are applied to the target networks. Performance evaluation reflects that the torus is a viable choice for small networks (32-64 nodes) and the Metacube exhibits similar performance to the hypercube for 128 nodes and 512 nodes networks under a moderate load. Lower link complexity and fewer long wires make the Metacube a cheaper alternative to the hypercube.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".