Exploiting Task- and Data-Level Parallelism in Streaming Applications Implemented in 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
This article describes the design and implementation of a novel compilation flow that implements circuits in FPGAs from a streaming programming language. The streaming language supported is called FPGA Brook and is based on the existing Brook language. It allows system designers to express applications in a way that exposes parallelism, which can be exploited through hardware implementation. FPGA Brook supports replication, allowing parts of an application to be implemented as multiple hardware units operating in parallel. Hardware units are interconnected through FIFO buffers which use the small memory modules available in FPGAs. The FPGA Brook automated design flow uses a source-to-source compiler, developed as a part of this work, and combines it with a commercial behavioral synthesis tool to generate the hardware implementation. A suite of benchmark applications was developed in FPGA Brook and implemented using our design flow. Experimental results indicate that performance of many applications scales well with replication. Our benchmark applications also achieve significantly better results than corresponding implementations using a commercial behavioral synthesis tool. We conclude that using an automated design flow for implementation of streaming applications in FPGAs is a promising methodology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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