Mapping applications on two-level configurable hardware
Bibliographic record
Abstract
Implementing applications on Reconfigurable Computing Architectures (RCAs) is an important research topic because of their high potential to accelerate a wide range of functions. Nevertheless, configuring and programming RCAs is a long-standing challenge. In this paper, we propose a design methodology to map an algorithm on an FPGA preconfigured with a Coarse-Grained Reconfigurable Architecture (CGRA). At the lowest configuration level, the architecture of the CGRA is elaborated, synthesized, placed and routed by some hardware design specialist using suitable tools. At the highest level, someone who has no particular knowledge in hardware design is however able to configure the CGRA in order to map his algorithm on a mesh of parallel computing and communicating nodes. Nevertheless, for medium and large applications, where the number of nodes varies from tens to thousands, getting good mapping of applications becomes manually intractable. Founded on well known mapping and routing algorithms that we have tailored to match our context, we propose a design methodology to automate the mapping of applications on a two-level configurable adaptive hardware fabric. Preliminary experiments on Fast Fourier Transform (FFT) and matrix multiplication applications show that the proposed methodology can lead to high throughput and/or low latency within a reasonable design time.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".