A Multi-Objective Model Oriented Mapping Approach for NoC-based Computing Systems
Bibliographic record
Abstract
In this paper, a multi-objective, i.e., reliability, communication energy, performance, co-optimization model oriented mapping approach is proposed to find optimal mappings when applications are mapped onto network-on-chip (NoC) based reconfigurable architectures. A co-optimization model, defined as reliability efficiency model (REM), is developed to evaluate the overall reliability efficiency of a mapping. In REM, reliability efficiency is defined as the reliability profit at the same energy latency product. Based on REM, a mapping approach, referred to as priority and compensation factor oriented branch and bound (PCBB), is introduced to figure out the best mapping pattern. Two techniques, priority allocation and compensation factor utilization, are adopted to make a tradeoff between search efficiency and accuracy. Experimental results show that the proposed approach has three major contributions compared to state-of-the-art approaches. (1) PCBB is highly efficient in finding best mappings, with a 3x and 720x speedup compared to branch and bound (BB) and simulated annealing (SA). (2) PCBB is able to dynamically remap after the reconfiguration of the architecture. (3) General quantitative evaluation for reliability, communication energy and performance are made respectively before integrated into the unified model REM, whereas other similar models only touch upon two of them quantitatively.
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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 | 0.001 |
| 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 | 0.001 |
| 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".