Multi-objective Tabu search based topology synthesis for designing power and performance efficient NoC architectures
Bibliographic record
Abstract
Network-on-Chip (NoC) communication interconnects have emerged as a solution to complex heterogeneous core systems such as those found in Multiprocessor System-on-Chip architectures. Many previous works have used the objectives of power or performance during topology synthesis for regular or application specific based NoC design. However, given the crucial requirements and demands of future on-chip applications, it is imperative that designs consider both power and performance aspects, in addition to other important system constraints. Therefore, in order to address such issues, this thesis work presents a multi-objective Tabu search based topology synthesis technique for designing power and performance efficient Network-on-Chip architectures. The methodology incorporates an analytical and simulation approach in order to compromise between computational time and effort within the algorithm. Furthermore, this work also presents a novel approach for a power and performance tradeoff during contention and deadlock removal within synthesis. The proposed method was tested using seven different multimedia and network benchmark application, where results displayed an increase in performance and decrease in power dissipation in comparison to other previous application specific and regular mesh designs. The analysis method was successful during topology generation, yielding an overall accuracy rate deviation of 19.8% within the worst case scenario.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".