Optimizing OpenCL Kernels and Runtime for DNN Inference on FPGAs
Why this work is in the frame
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Bibliographic record
Abstract
We explore a compilation flow for the generation of DNN accelerators on FPGAs. The flow translates a frozen model into OpenCL kernels with the TVM compiler and uses the Intel OpenCL compiler to generate RTL code. We improve the quality of the generated hardware with optimizations that increase parallelism, reduce memory access latency, increase concurrency and reuse kernels to save on-chip memory. We conduct a preliminary evaluation by manually applying the optimizations for LeNet-5. The evaluation shows that the optimizations improve the performance of the generated accelerators by up to 8.48× over the unoptimized accelerator. The performance of the most optimized accelerator we generate is 4.79× faster than Tensorflow on an Intel i9 CPU. These results encourage us to automate these optimizations in TVM for public release.
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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 | 0.000 |
| 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