SoC memory optimization using loop transformations
Bibliographic record
Abstract
In today's embedded systems, the memory hierarchy is rapidly becoming a major factor in terms of power, performance and area. This is especially true for embedded multimedia applications using temporary multi-dimensional arrays that are typically used to store intermediate results during multimedia processing. In this paper, we introduce a new buffer allocation method to replace these temporary arrays and we combine it with loop fusion and tiling. The simple and effective method we present simultaneously applies tiling with fusion to a set of loop nests. Then, it replaces temporary arrays with smaller buffers containing the useful data. These new techniques allow to optimize memory space and reduce the number of cache misses. Our buffer allocation method is implemented in the PIPS compiler and the experiments are made on the StepNP simulator. They show that our technique yields a significant reduction in the number of data cache misses (on average, the data cache miss ratio is decreased by 14.3%)
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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 | 0.001 |
| 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".