Multi-Swarm based NoC Configuration and Synthesis
Bibliographic record
Abstract
Network-on-Chip (NoC) is a popular interconnection structure suited to many-core System-on-Chip (SoC). Assuming a mesh-based NoC, we explore the assignment of cores to NoC nodes and produce a best NoC configuration having minimal communication traffic, power consumption, and chip area. We employ pre-synthesized NoC components data to estimate power and area consumption of the interconnection network. NoC configuration and mapping problem is NP-hard, and we propose a hybrid scheme of swarm optimization that combines Tabu-list, sub-swarms, and Discrete Particle Swarm Optimization (DPSO). The main goal is to configure and synthesize NoC such that the total NoC latency, power consumption, and chip area are minimal. DPSO is used as the main optimization scheme and modified it such that each swarm particle move is influenced by NoC traffic. The methodology is tested for some multimedia application core graphs. It is determined that on average our tool reduced NoC area by 30% on average and reduced total NoC power (static + worst case dynamic) by 27.5% as compared to unoptimized NoCs.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".