MLIR-to-CGRA: A Versatile MLIR-Based Compiler Framework for CGRAs
Bibliographic record
Abstract
Coarse-grained reconfigurable architectures (CGRAs) are programmable hardware platforms with coarse-grained programmable logic blocks and word-wide configurable interconnect. In this paper, we describe a high-level compilation framework tailored for CGRAs. The input to the framework is a C-language program and description of the available CG RA architectural features. This work primarily focuses on automatically handling sequential rectangular loop access patterns. It achieves this by running automated design space exploration (DSE) written within the MLIR compiler representation [7] to leverage both spatial and temporal parallelism inherent in CG RAs. Furthermore, the compiler is engineered to be architecture-agnostic. It employs standard loop (and other) compiler optimization passes alongside CG RA-specific passes to generate a data flow graph (DFG) tailored for a CG RA implementation. In an experimental study, we optimize kernels to increase CG RA mappability and show the impact of automated DSE on the kernel performance. Moreover, the framework eases the task of compilation from software to RISC-V+CGRA hybrid systems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".