Demand Paging Techniques for Flash Memory Using Compiler Post-Pass Optimizations
Bibliographic record
Abstract
In this article, we propose an application-specific demand paging mechanism for low-end embedded systems that have flash memory as secondary storage. These systems are not equipped with virtual memory. A small memory space called an execution buffer is used to page the code of an application. An application-specific page manager manages the buffer. The page manager is automatically generated by a compiler post-pass optimizer and combined with the application image. The post-pass optimizer analyzes the executable image and transforms function call/return instructions into calls to the page manager. As a result, each function in the code can be loaded into the memory on demand at runtime. To minimize the overhead incurred by the demand paging technique, code clustering algorithms are also presented. We evaluate our techniques with ten embedded applications, and our approach can reduce the code memory size by on average 39.5% with less than 10% performance degradation and on average 14% more energy consumption. Our demand paging technique provides embedded system designers with a trade-off control mechanism between the cost, performance, and energy efficiency in designing embedded systems. Embedded system designers can choose the code memory size depending on their cost, energy, and performance requirements.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".