LeFlow: Automatic Compilation of TensorFlow Machine Learning Applications to 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
Acceleration of Machine Learning applications on Field-Programmable Gate Arrays (FPGAs) has shown to have advantages over other computing platforms in recent work. However, since machine learning code is often specified in a high-level software language such as Python, the manual translation of the algorithm to either C code for high-level synthesis or to Register Transfer Level (RTL) code for synthesis is time consuming and requires the designer to have expertise in designing hardware. In order to show how we can make FPGAs more accessible to software developers, we present a demonstration of LeFlow: an open-source tool which maps numerical computation models written in TensorFlow to synthesizable RTL. This demonstration includes two examples which begin with a model written in TensorFlow and show how a designer would use the LeFlow tool to generate Verilog, simulate the result, and synthesize the design to target FPGAs.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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