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
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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