Mocarabe: High-Performance Time-Multiplexed Overlays for FPGAs
Bibliographic record
Abstract
Coarse-grained reconfigurable array (CGRA) overlays can improve dataflow kernel throughput by an order of magnitude over Vivado HLS. This is possible with a combination of carefully floorplanned high-frequency (645-768 MHz) design and a scalable, communication-aware compiler. Our 2D torus CGRA architecture supports versatile processing element (PE) functionality and a configurable number of communication channels to match application demands. Compared to recent FPGA overlays like CGRA-ME's ADRES and HyCUBE implementations, our design operates at 1.8-3.4× faster clock frequency, while scaling to an orders-of-magnitude larger array size of 19×69 on Xilinx Alveo U280. Our communication-aware compiler targets HLS objectives such as initiation interval (II) and minimizes communication cost using an integer linear programming (ILP) formulation. Unlike SDC schedulers in FPGA HLS tools, we treat data movement as a first-class citizen by encoding the space and time resources communication network of the overlay in the ILP formulation. Given the same constraints on operational resources as Vivado HLS, we can retain our target II and achieve up to 9.2× higher frequency. Our ILP scheduler outperforms a PathFinder space-time router implementation in quality of result by up to nearly 2×.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".