Exploiting Stable Data Dependency in Stream Processing Acceleration on FPGAs
Bibliographic record
Abstract
With the unique feature of fine-grained parallelism, field-programmable gate arrays (FPGAs) show great potential for streaming algorithm acceleration. However, the lack of a design framework, restrictions on FPGAs, and ineffective tools impede the utilization of FPGAs in practice. In this study, we provide a design paradigm to support streaming algorithm acceleration on FPGAs. We first propose an abstract model to describe streaming algorithms with homogeneous sub-functions (HSF) and stable data dependency (SDD), which we call the HSF-SDD model. Using this model, we then develop an FPGA framework, PE-Ring, that has the advantages of (1) fully exploiting algorithm parallelism to achieve high performance, (2) leveraging block RAM to serve large scale parameters, and (3) enabling flexible parameter adjustments. Based on the proposed model and framework, we finally implement a specific converter to generate the register-transfer level representation of the PE-Ring. Experimental results show that our method outperforms ordinary FPGA design tools by one to two orders of magnitude. Experiments also demonstrate the scalability of the PE-Ring.
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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 | 0.001 |
| 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".