Bibliographic record
Abstract
Network-on-Chip (NoC) has been proposed as an interconnection framework for connecting large number of cores for a System-on-Chip (SoC). Assuming a mesh-based NoC, we explore the assignment of cores to cross-points and produce a best NoC configuration with minimum average communication traffic, power consumption and chip area. We use pre-synthesized network components data to estimate power and chip area of the NoC. NoC configuration and mapping problem belongs to NP-hard complexity set, therefore we propose a hybrid scheme of swarm optimization that combines Tabu-search, force-directed swapping, sub-swarms, and Discrete Particle Swarm Optimization (DPSO). The main goal of the optimization is to configure the NoC such that the total NoC latency, power consumption, and area occupied are minimal. DPSO is used as the main optimization scheme and modified so that each particle move is also influenced by a force derived from the NoC traffic matrix. The methodology is tested for some multimedia application core graphs as well as large network of randomly generated cores. It is determined that on average our hybrid technique required less number of iterations and time to reach an optimal solution when compared with existing NoC synthesis algorithms.
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".