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在Zynq-7000 FPGA上设计和实现YOLOv2加速器
1Department of Electronic Engineering, Gachon University, Seongnam 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究优化了You Only Look Once (YOLOv2) 对象检测算法,用于低功耗的可编程门阵列 (FPGA). 高效的设计显著减少了Zynq-7000 SoC上的硬件资源使用,精度损失最小.
科学领域:
- 计算机工程 计算机工程
- 人工智能的人工智能
- 嵌入式系统 嵌入式系统
背景情况:
- 像You Only Look Once (YOLO) 这样的对象检测算法对于实时视觉至关重要,但需要高计算能力.
- 图形处理单元 (GPU) 耗电密集,因此不适合低功耗应用.
- 现场可编程网关数组 (FPGA) 提供了一种节能替代方案,但需要专门的优化才能高效地实现.
研究的目的:
- 为Zynq-7000系统芯片 (SoC) 开发一个优化的YOLOv2加速器.
- 为了提高资源效率和减少电力消耗,在FPGA上部署YOLOv2.
- 为了保持高精度,同时最大限度地减少硬件资源的利用.
主要方法:
- 使用16位整数量化实现了YOLOv2.
- 采用过器重复使用结构和输入特征地图重复使用方案,带有线路缓冲区.
- 优化了卷积和最大聚合层的板参数.
- 引入了一个基于停机的控制机制,以防止管道危险.
主要成果:
- 与32位浮点基线相比,实现了0.2%的微不足道的精度下降.
- 与以前的YOLO加速器相比,在相同的SoC上,翻转式 (FF) 和数字信号处理器 (DSP) 的使用量分别减少了26%和15%.
- 在XC7Z020上展示了可行的部署,利用57.27%的DSP和16.55%的FF资源.
结论:
- 优化的YOLOv2加速器为FPGA上的实时对象检测提供了节能和资源高效的解决方案.
- 该设计成功地平衡了Zynq-7000 SoC上的性能,准确性和硬件资源利用率.
- 这项工作使得先进的对象检测模型能够在资源受限的嵌入式系统中实际部署.
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