轻量U-Net实现实时航拍图像分割，对比三类硬件部署效果
Real-Time Semantic Segmentation of Aerial Images Using an Embedded U-Net: A Comparison of CPU, GPU, and FPGA Workflows
- 压缩模型参数与计算量16倍，保持原有精度
- FPGA+Vitis AI在延迟、能效上表现最佳
- 适合嵌入式部署需权衡性能与开发难度
本研究提出一种轻量级U-Net模型，专为实时航拍图像语义分割设计，适用于商用现成（COTS）嵌入式平台。在真实数据集上保持原U-Net精度的同时，将模型参数和乘累加（MAC）操作减少16倍。全面评估了三种硬件平台（CPU、GPU、FPGA）及五种工具链（TVM、FINN、Vitis AI、TensorFlow GPU、cuDNN），涵盖延迟、功耗、内存占用、能效和FPGA资源使用等指标。结果表明，尽管FPGA配合Vitis AI在性能、能效和成熟度上最优，但需专业知识支持，凸显嵌入式语义分割解决方案选择中的平衡必要性。
原文摘要 · Abstract (English)
This study introduces a lightweight U-Net model optimized for real-time semantic segmentation of aerial images, targeting the efficient utilization of Commercial Off-The-Shelf (COTS) embedded computing platforms. We maintain the accuracy of the U-Net on a real-world dataset while significantly reducing the model's parameters and Multiply-Accumulate (MAC) operations by a factor of 16. Our comprehensive analysis covers three hardware platforms (CPU, GPU, and FPGA) and five different toolchains (TVM, FINN, Vitis AI, TensorFlow GPU, and cuDNN), assessing each on metrics such as latency, power consumption, memory footprint, energy efficiency, and FPGA resource usage. The results highlight the trade-offs between these platforms and toolchains, with a particular focus on the practical deployment challenges in real-world applications. Our findings demonstrate that while the FPGA with Vitis AI emerges as the superior choice due to its performance, energy efficiency, and maturity, it requires specialized hardware knowledge, emphasizing the need for a balanced approach in selecting embedded computing solutions for semantic segmentation tasks
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