Bibliographic record
Abstract
Course-Grained Reconfigurable Architectures (CGRAs) are programmable hardware platforms with coarse-grained programmable logic blocks and word-wide configurable interconnect. In this thesis, we present a high-level compilation framework for the processor + CGRA hybrid systems, where a CGRA is used in tandem with the processor for application acceleration. The input to the framework is a C-language program. The output is a compiled executable for a RISC-V processor, and an optimized dataflow graph (DFG) kernel for acceleration on the CGRA. We utilize the powerful MLIR compiler representation to realize such optimization, mainly targeting sequential rectangular loop access patterns and leveraging the spatial parallelism of the CGRA. We take inspiration from ScaleHLS, which is a compiler exploration framework for high-level synthesis (HLS) on FPGAs. In an experimental study, we show the impact of our kernel optimizations on CGRA performance. Our automatic exploration compiler pass is able to raise kernel throughput, while increasing kernel mappbility. With our automatic generation framework, we aim to take the burden off users from manually crafting both the assembly and kernel representation to run an application on a RISC-V+CGRA hybrid system, thereby raising designer productivity and reducing cost.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".