An FPGA-based processor for training convolutional neural networks
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Bibliographic record
Abstract
Convolutional neural networks (CNNs) have gained great success in various computer vision applications. However, training a CNN model is computation-intensive and time-consuming. Hence training is mainly processed on large clusters of high-performance processors like server CPUs and GPUs. In this paper, we propose an FPGA-based processor design to accelerate the training process of CNNs. We first analyze the operations in all types of CNN layers in the training process. A uniform computation engine design is proposed to efficiently carry out all kinds of operations based on the analysis. Then a scalable accelerator framework is presented that exploits the parallelism further by unrolling the loops in two levels. The proposed accelerator design is demonstrated by implementing a processor on the Xilinx ZU19EG FPGA working at 200 MHz. The evaluation results on a group of CNN models show that our processor is 5.7 to 10.7-fold faster than the software implementations on the Intel Core i5-4440 CPU(@3.10GHz).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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