Performance evaluation of three Network-on-Chip (NoC) architectures (Invited)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it