AN ENHANCED GENETIC ALGORITHM FOR SOLVING THE HIGH-LEVEL SYNTHESIS PROBLEMS OF SCHEDULING, ALLOCATION, AND BINDING
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Bibliographic record
Abstract
This paper presents a novel approach to the concurrent solution of three High-Level Synthesis (HLS) problems that are modeled as a Constraint-Satisfaction Problem (CSP) and solved using an Enhanced Genetic Algorithm (EGA). We focus on the core problems of high-level synthesis: Scheduling, Allocation, and Binding. Scheduling consists of assigning of operations in a Data-Flow Graph (DFG) to control steps or clock cycles. Allocation selects specific numbers and types of functional units from a hardware library to perform the operations specified in the DFG. Binding assigns constituent operations of the DFG to specific unit instances. A very general version of this problem is considered where functional units may perform different operations in different numbers of control steps. The EGA is designed to solve CSPs quickly and does not require a user to specify appropriate mutation and crossover rates a priori; these are determined automatically during the course of the genetic search. The enhancements include a directed mutation operator and a new type of elitism that avoids premature convergence. The HLS problems are solved by applying two EGAs in a hierarchical manner. The first performs allocation, while the second performs scheduling and binding and serves as the fitness function for the second. When compared to other, well-known techniques, our results show a reduction in time to obtain optimal solutions for standard benchmarks.
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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 | 0.001 | 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 | 0.001 | 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