A dynamic programming approach to complex allocation in a DSP pipelined processor
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Bibliographic record
Abstract
This paper describes a deterministic non-serial dynamic programming technique applicable to a code optimization problem for the Star Core 140 (SC140) DSP processor. The code optimization problem analyzed is an optimal register allocation problem that minimizes the expected number of execution sets with a two-word prefix for the SC140 core applications based on two probabilistic allocation policies. We introduce two basic algorithms, a linear bridging algorithm and a non-linear bridging algorithm (supporting loops), that solve the specific register allocation problem for an assembly language code block. All algorithms and methods are applied to a variety of SC140 assembly code applications. The optimized assembly language codes generated show an average of 68%, improvement in overheads and an average of 4.44% code size reduction at a very small increase in the CPU time costs. The basic principles and methods developed throughout this research are general and are applicable to other pipelined processors.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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