CGRA-ME: An Open-Source Framework for CGRA Architecture and CAD Research : (Invited Paper)
Bibliographic record
Abstract
Coarse-grained reconfigurable arrays (CGRAs) are programmable hardware platforms that can be used to realize application-specific accelerators for higher performance and energy efficiency. A CGRA is a 2D array of configurable logic blocks & interconnect, where the logic blocks are typically large & ALU-like, and the interconnect is word-wide. CGRA-ME is a software framework that enables the modelling and exploration of CGRA architectures, as well as research on CGRA CAD algorithms. With CGRA-ME, an architect can specify a CGRA architecture at a high level of abstraction. A set of applications can be mapped onto the architecture to assess the mappability, power, performance and cost. CGRA-ME also allows one to generate synthesizable Verilog RTL for the modelled CGRA, permitting its implementation as an ASIC or FPGA overlay. In this paper, we describe the CGRA-ME framework [5] and overview its capabilities and current limitations. We discuss ongoing and prior research conducted with the framework, as well as outline future plans. We believe CGRA-ME will be a valuable contribution to the community, enabling new research on CGRA CAD & architectures.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.032 |
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".