Mapping Applications to Coarse-Grained Reconfigurable Architectures
Bibliographic record
Abstract
Coarse grain reconfigurable architectures (CGRAs) are capable of achieving both goals of high performance and flexibility. CGRAs not only improve performance by exploiting the features of repetitive computations but also can adapt to diverse computations by dynamically changing configurations of an array of its internal processing elements (PEs) and their interconnections. Many CGRAs have been developed recently as programmable coprocessors, minimizing the overhead of the central processor in many computation-intensive applications. They act as co-processors for accelerating computation-intensive portions of embedded system applications. System designers are attracted to CGRAs because they bridge the gap between Application Specific Integrated Circuits (ASICs) and microprocessors by providing the high performance of ASICs with flexibility of reconfiguration of fine-grained FPGAs. Some of the application areas of these architectures are image processing, DSP, encryption, pattern recognition, and other multimedia applications. This paper surveys the methods of compiling applications to coarse grain reconfigurable architectures.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".