Statically Scheduled vs. Elastic CGRA Architectures: Impact on Mapping Feasibility
Bibliographic record
Abstract
Coarse-grained reconfigurable architectures (CGRAs) are programmable hardware platforms with large ALU-like programmable logic blocks and word-wide configurable interconnect. In statically scheduled CGRAs, operations within an application’s dataflow graph (DFG) are scheduled to occur in specific clock cycles. The specific schedule may mandate register insertion on certain DFG edges so that DFG paths are correctly latency balanced. In elastic (dataflow) CGRAs, no scheduling step is executed, as handshaking is used to trigger a specific DFG operation when its inputs arrive. In this work, we present a mapper that can map applications to static and elastic architectures and study the impact of the static vs. elastic paradigms on CGRA mapping feasibility, and specifically, the consequences of these paradigms on the use of CGRA resources in mapped applications. Experimental results, targeting the popular HyCUBE [1] and ADRES [2] CGRA architectures, show that applications mapped onto statically scheduled CGRAs generally require larger array sizes and have longer interconnect paths relative to the same applications mapped onto elastic CGRAs, with the bloat arising from additional registers to balance path latencies.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".