Optimizing Deep Learning Acceleration on FPGA for Real-Time and Resource-Efficient Image Classification
Deep learning (DL) has revolutionized image classification, yet deploying convolutional neural networks (CNNs) on edge devices for real-time applications remains a significant challenge due to constraints in computation, memory, and power efficiency. This work presents an optimized implementation of...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/1/422 |
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| Summary: | Deep learning (DL) has revolutionized image classification, yet deploying convolutional neural networks (CNNs) on edge devices for real-time applications remains a significant challenge due to constraints in computation, memory, and power efficiency. This work presents an optimized implementation of VGG16 and VGG19, two widely used CNN architectures, for classifying the CIFAR-10 dataset using transfer learning on field-programmable gate arrays (FPGAs). Utilizing the Xilinx Vitis-AI and TensorFlow2 frameworks, we adapt VGG16 and VGG19 for FPGA deployment through quantization, compression, and hardware-specific optimizations. Our implementation achieves high classification accuracy, with Top-1 accuracy of 89.54% and 87.47% for VGG16 and VGG19, respectively, while delivering significant reductions in inference latency (7.29× and 6.6× compared to CPU-based alternatives). These results highlight the suitability of our approach for resource-efficient, real-time edge applications. Key contributions include a detailed methodology for combining transfer learning with FPGA acceleration, an analysis of hardware resource utilization, and performance benchmarks. This work underscores the potential of FPGA-based solutions to enable scalable, low-latency DL deployments in domains such as autonomous systems, IoT, and mobile devices. |
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| ISSN: | 2076-3417 |