FPGA-based training of convolutional neural networks with a reduced precision floating-point library
Bibliographic record
Abstract
Convolutional Neural Networks (CNNs) have been shown to have high accuracy for classification tasks in numerous applications, which has resulted in their widespread adoption. However, the high accuracy of CNNs comes at the cost of high compute and bandwidth requirements for both classification and training. In this work we discuss an FPGA-based CNN training engine: FCTE, implemented using High-Level Synthesis (HLS), targeting the Xilinx Kintex Ultrascale XCKU115 device. Furthermore, we detail custom-precision floating-point (CPFP) cores for multiplication and addition implemented using HLS, which allows for reduced area utilization. We use these cores with our engine to train networks to demonstrate that an exponent width of 6 and mantissa width of 5 achieves accuracy comparable to single-precision floating-point for the MNIST and CIFAR-10 datasets. These results are achieved using round-to-zero for the CPFP multipliers and round-to-nearest for the CPFP adders, allowing for LUT savings of 32.6% for the multipliers and 21.7% for the adders when compared to half-precision floating-point, while using the same number of DSPs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".