RAAP-CGRA: Placement for CGRAs with Restricted Routing Architectures
Bibliographic record
Abstract
Coarse-grained reconfigurable arrays (CGRAs) are programmable hardware devices that are word-level configurable, and can be used to implement application-specific accelerators, particularly for applications that can benefit from spatial and pipeline parallelism. A considerable portion of a CGRA’s silicon area is dedicated to realizing programmability, and it is desirable to reduce this overhead, while retaining flexibility and application mappability. This work considers CAD techniques for CGRAs with reduced interconnect flexibility. Specifically, we introduce Routing-Architecture-Aware Placement for CGRAs, RAAP-CGRA, comprising CGRA placement schemes suitable for CGRAs with restricted routing architectures. At a high level, the proposed schemes penalize placements likely to be unroutable due to architectural routing constraints. Experiments on three constrained CGRA architectures show our approaches significantly improve the success rate of mapping compared to a simulated-annealing-based baseline. For one CGRA architecture with restricted routing, a baseline mapper had an 11% average success rate, while one of the proposed mappers achieved a 61% success rate.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".