Bibliographic record
Abstract
A key aspect of Coarse-Grained Reconfigurable Arrays (CGRAs) is dynamic reconfigurability, where multiple configurations, or contexts, are loaded into the CGRA to time-multiplex its resources. This feature allows the CGRA to accommodate larger applications without a significant increase in its size. Context switching is typically centralized, using the system clock to synchronously cycle through configurations simultaneously across CGRA resources. This approach is unable to efficiently accommodate variable-latency operations. Elastic CGRAs were proposed to handle such operations via an architecture that operates according to a dataflow paradigm. However, elastic solutions are single context by nature, which limits their applicability to smaller application kernels. Time-multiplexed multi-context and elastic CGRAs are thus naturally incompatible with one another. In this paper, we aim to overcome this incompatibility and propose an architectural framework that is capable of generating elastic CGRAs with multi-context support. Elastic primitives that traverse contexts in a distributed fashion are introduced. We also extend conventional mapping solutions to handle the new architectures. Finally, we evaluate the area and performance overhead for elastic multi-context CGRAs over single context ones with equal processing capacity.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".