A Study on the Design Procedure of Re-Configurable Convolutional Neural Network Engine for FPGA-Based Applications
Bibliographic record
Abstract
Convolutional neural networks (CNNs) have become a primary approach in the field of artificial intelligence (AI), with wide range of applications. The two computational phases for every neural network are; the training phase and the testing phase. Usually, testing is performed on high-processing hardware engines, however, the training part is still a challenge for low-power devices. There are several neural accelerators; such as graphics processing units and field-programmable-gate-arrays (FPGAs). From the design perspective, an efficient hardware engine at the register-transfer level and efficient CNN modeling at the TensorFlow level are mandatory for any type of application. Hence, we propose a comprehensive, and step-by-step design procedure for a re-configurable CNN engine. We used TensorFlow and Keras libraries for modeling in Python, whereas the register-transfer-level part was performed using Verilog. The proposed idea was synthesized, placed, and routed for 180 nm complementary metal-oxide semiconductor technology using synopsis design compiler tools. The proposed design layout occupies an area of 3.16 × 3.16 mm2. A competitive accuracy of approximately 96% was achieved for the Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research (CIFAR-10) datasets.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".