A high-performance overlay architecture for pipelined execution of data flow graphs
Bibliographic record
Abstract
A major issue facing the widespread use of FPGAs as accelerators is their programmability wall: the difficulty of hardware design and the long synthesis times. Overlays-pre-synthesized FPGA circuits that are themselves reconfigurable - promise to tackle these challenges. We design and evaluate an overlay architecture, structured as a mesh of functional units, for pipelined execution of data-flow graphs (DFGs), a common abstraction for expressing parallelism in applications. We use data-driven execution based on elastic pipelines to balance pipeline latencies and achieve a high fMAX, scalability and maximum throughput. We prototype two overlays on a Stratix IV FPGA: a 355 MHz 24×16 integer overlay and a 312 MHz 18×16 floating-point overlay. We also design a tool that maps DFGs to overlays. We map 15 DFGs and show that the two overlays deliver throughputs of up to 35 GOPS and 22 GFLOPS, respectively. We also show that DFG mapping is fast, taking no more than 6 seconds for the largest DFG. Thus, our overlay architecture raises the level of abstraction of FPGA programming closer to that of software and avoids lengthy synthesis time, easing the use of these devices to accelerate applications.
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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 | 0.001 |
| 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) | 0.003 | 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".