Optimization of Compiler-Generated OpenCL CNN Kernels and Runtime for FPGAs
Bibliographic record
Abstract
We translate frozen CNN models into OpenCL kernels with the TVM compiler and then use Intel's OpenCL SDK to compile to an FPGA bitstream. We improve the performance of the generated base hardware with optimizations that increase parallelism, reduce memory access latency, and save on-chip resources. We automate these optimizations in TVM and evaluate them by generating accelerators for LeNet-5, MobileNetV1 and ResNet-34 on an Intel Stratix 10SX. The optimizations improve the performance of the generated accelerators by up to 846 × over the base ones. The optimized accelerators are up to 4.57 × faster than TensorFlow on CPU, 3.83 × faster than single-threaded TVM and are only 0.34 × slower than TVM with 56 threads. Our optimized kernels also outperform ones generated by similar approaches that use high-level synthesis, but they underperform ones that utilize hand-optimized designs. Thus, our approach is most useful in environments that benefit from increased performance and fast prototyping, realizing the benefits of FPGAs without hardware design expertise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".