Modeling and Exploration of Elastic CGRAs
Bibliographic record
Abstract
Elastic design concepts have the potential to bring multiple benefits to coarse-grained reconfigurable arrays (CGRAs) architecture, including the ability to interface with memories, having unknown latencies, incorporate run-time variable-latency processing elements, and ease the CGRA mapping challenges of scheduling, placement and routing. However, there are overheads in terms of power, performance and area (PPA) associated with the design and implementation of elastic circuits. In this paper, we quantify these overheads in the CGRA context by first extending an open-source CGRA modelling and exploration framework (CGRA-ME) [4] to allow elastic circuit primitives (e.g. fork, join, merge, diverge, etc.) to be used when composing/modelling a CGRA architecture. We then use this new capability to “elasticize” two widely studied CGRA architectures, ADRES [11] and HyCUBE [8]. The PPA of the elastic versions of the CGRAs are compared with their traditional statically scheduled counterparts. We also evaluate the PPA “cost” of several elastic-circuit design points, such as elastic buffer length and inclusion of merge and diverge components.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".