Resource Efficient Image Super-Resolution for FPGA-Based Optimized Deep Learning – An Innovative Target Detection Model
Bibliographic record
Abstract
The Resource Efficient Image Super-Resolution for FPGA-Based Optimized Deep Learning – An Innovative Target Detection Model (REISFD) model is a deep learning model that uses FPGA acceleration for real-time image classification on devices with limited resources. It merges advanced convolutional neural networks with FPGA optimizations to ensure fast and energy-efficient performance without sacrificing accuracy. The model is designed for 10-class tasks using the CIFAR-10 dataset and relies on pre-trained VGG16 and VGG19 networks that have been improved through techniques like data augmentation and normalization. The model uses Xilinx's DPUCZDX8G for efficiency and low power on an Avnet Ultra96-V2 board with a Zynq UltraScale MPSoC FPGA. It includes optimized LSTM structures to process sequential data. Evaluation results show that REISFD outperforms traditional models in classification accuracy while minimizing hardware needs and inference times. REISFD model achieves over 90% accuracy in most CIFAR-10 classes, making it ideal for IoT, embedded AI, and edge computing applications, showcasing the benefits of deep learning with FPGA acceleration.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".