Integrating Memory Optimization with Mapping Algorithms for Multi-Processors System-on-Chip
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
Due to their great ability to parallelize at a very high integration level, Multi-Processors Systems-on-Chip (MPSoCs) are good candidates for systems and applications such as multimedia. Memory is becoming a key player for significant improvements in these applications (power, performance and area). The large amount of data manipulated by these applications requires high-capacity computing and memory. Lately, new programming models have been introduced. This leads to the need of new optimization and mapping techniques suitable for embedded systems and their programming models. This article presents novel approaches for combining memory optimization with mapping of data-driven applications while considering anti-dependence conflicts. Two different approaches are studied and integrated with existing mapping algorithms. The first approach (based on heuristic algorithms) keeps the graph transformation for memory optimization stage from the mapping stage and enables their combination in a design flow. The second approach (based on evolutionary algorithms) combines these two stages and integrates them in a unique stage. Some significant improvements are obtained for memory gain, communication load and physical links.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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