Mapping Enumeration for Multi-Context CGRAs Using Zero-Suppressed Binary Decision Diagrams
Bibliographic record
Abstract
A primary aim of Coarse-Grained Reconfigurable Arrays (CGRAs), compared to FPGAs, is to maximize the portion of the die used for computational resources, while minimizing the complexity of control and steering logic, leading to inherently constrained routing architectures. This challenge has compelled CAD developers to utilize exact solutions, such as integer linear programming (ILP), in formulating and solving the mapping problem. Those solutions have been shown not to scale, especially for larger devices with intricate architectural features, such as multiple contexts and optional pipeline registers. Even if an exact or a greedy approach yields a feasible solution, it often fails to optimize multifaceted objective criteria. In this work, we have devised a framework for systematically enumerating mapping solutions of a subject kernel on a target CGRA using Zero- Suppressed Binary Decision Diagrams (ZDDs). To effectively manage runtime, we developed a linear algorithm that retains the best$k$solutions at each stage of the mapping flow, where both the objective function and$k$are user defined. Experimental results on a diverse range of application kernels targeting two CGRA architectures show how we can enumerate hundreds of thousands of solutions within seconds. When compared against prior methodologies, and while generating dozens of solutions, our mapper exhibits a remarkable speed advantage, ranging from one to three orders of magnitude faster than exact and heuristic approaches. Notably, when allocated the same runtime as the fastest heuristic, our framework demonstrates its efficacy by generating an impressive 105 solutions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".