FRESCO: Efficient Subgraph Enumeration for Scalable Clustering in Heterogeneous CGRAs
Bibliographic record
Abstract
In recent years, there has been a trend towards reconfigurable fabrics at the intersection between field-programmable gate arrays (FPGAs) and coarse-grained reconfigurable arrays (CGRAs): using FPGA-like interconnect but word-based and built around coarse-grained primitives. These architectures often employ complex clusters with far more heterogeneous resources than FPGAs or typical CGRAs— sometimes over a hundred primitives, most of which are bypassable. As a result, clustering, the problem of covering the application netlist with architecture clusters, is a key challenge for design tools targeting these fabrics. Clustering is analogous to the instruction selection problem in CISC architectures, albeit with orders of magnitude more complex "instructions". In this work, we propose a two-phase, architecture-agnostic clustering algorithm that scales to highly complex architecture clusters. The first phase enumerates potential cluster matches in the application netlist using a strategy based on an abstract decision tree. The second phase selects a cover from the enumerated matches. We show that our algorithm effectively prunes the search space for complex clusters, scales well to circuits composed of many clusters, and achieves better clustering quality than CLUMAP, a state-of-the-art CGRA clustering algorithm, for the simple cases that CLUMAP can handle.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".