Exploiting Stable Data Dependency in Stream Processing Acceleration on FPGAs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.007 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it