Instruction-set matching and GA-based selection for embedded-processor code generation
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
The core tasks of retargetable code generation are instruction-set matching and selection for a given application program and a DSP/ASIP processor. In this paper, we utilize a model of target architecture specification that employs both behavioral and structural information, to facilitate this process. The matching method is based on a pattern tree structure of instructions. This tree structure, generated automatically, is implemented by using a pattern queue and a flag table. The matching process is efficient since it bypasses many patterns in the tree which do not match at certain nodes in the DFG of given application program. Two genetic algorithms are implemented for pattern selection: a pure GA which uses standard GA operators, and a GA with backtracking which employs variable-length chromesomes. Optimal or near-optimal pattern selection is obtained in a reasonable period of time for a wide range of application programs.
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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 it