Comparing a genetic algorithm penalty function and repair heuristic in the DSP application domain
Why this work is in the frame
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Bibliographic record
Abstract
To increase memory bandwidth, many programmable Digital Signal Processors (DSPs) employ two on-chip data memories. This architectural feature supports higher memory bandwidth by allowing multiple data memory accesses to occur in parallel. Exploiting dual memory banks, however, is a challenging problem for compilers. This, in part, is due to the instruction-level parallelism, small numbers of registers, and highly specialized register capabilities of most DSPs. In this paper, we present a new methodology based on a genetic algorithm (GA) for assigning data to dual-bank memories. Our approach is global, and integrates several important issues in memory assignment within a single model. Special effort is made to identify those data objects that could potentially benefit from an assignment to a specific memory, or perhaps duplication in both memories. As part of our experimentation, we compare the effectiveness of a repair heuristic which consist in transforming infeasible solutions into feasible ones, with a penalty functions that seeks to degrade the fitness of infeasible individuals based on their degree of constraint violation. Our results show that the repair operator out-performs the penalty function. Tests on DSPstone benchmarks show that the GA is able to achieve a 54% reduction in the number of memory cycles and a reduction in the range of 7% to 42% in the total number of cycles.
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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 | 0.000 |
| 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