CGRA Mapping Using Zero-Suppressed Binary Decision Diagrams
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The restricted routing networks of coarse-grained reconfigurable arrays (CGRAs) have motivated CAD developers to utilize exact solutions, such as integer linear programming (ILP), in formu-lating and solving the mapping problem. Such so-lutions that rely on general purpose optimizers have not been shown to scale. In this work, we formu-late CGRA mapping as a solution enumeration and selection problem, relying on the efficiency of zero-suppressed binary decision diagrams (ZDDs) [22] to capture the solution space. For small-to-moderate size problems, it is possible to capture every possible map-ping in a few megabytes. For larger problems, thou-sands if not millions of solutions can be enumerated. The final mapping is a simple linear-time DAG traver-sal of the enumeration ZDD. The proposed solution was implemented in the CGRA-ME [6] framework. A speedup of two orders of magnitude was obtained when compared with past solutions targeting smaller CGRA devices. Larger devices beyond the capacity of those solutions are now accessible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 0.000 |
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 it