Efficient Data Streaming for a Tightly-Coupled Coarse-Grained Reconfigurable Array
Bibliographic record
Abstract
We propose, implement and evaluate a data streaming unit (DSU) for a Coarse-Grained Reconfigurable Array (CGRA) that is tightly coupled to its host CPU. The DSU accesses system memory at the granularity of cache lines and streams data to CGRA cells that perform loads, and conversely from CGRA cells that perform stores. The unit supports both direct and indirect accesses, as well as dynamic accesses in which the access location is computed on the CGRA. Further, the DSU can exploit cache line reuse to feed (collect) data on the same cache line to (from) multiple CGRA cells, reducing the number of memory transactions. A prototype DSU is implemented on an Intel Stratix 10MX FPGA that is connected to external DDR4 memory. Evaluation shows that ideal data streaming throughput is achieved for common direct accesses, and for indirect ones when sparsity structure is present. Evaluation also shows that the DSU is effective in exploiting cache line reuse. Thus, we conclude that direct data streaming between memory and CGRA cells is effective.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".