一个基于FPGA的YOLOv5加速器,用于实时工业视觉应用
Zhihong Yan1,2, Bingqian Zhang1,2, Dong Wang1,2
1Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China.
Micromachines
|September 28, 2024
概括
我们为YOLOv5物体检测模型开发了一种新的4位量子化加速器,大大提高了工业应用的速度和效率. 这种新设计增强了实时处理,而不会影响准确度.
科学领域:
- 计算机视觉 计算机视觉
- 硬件加速器 硬件加速器
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 你只看一次 (YOLO) 的物体检测网络因其速度和准确性在工业中广泛使用.
- 现有的8位量子化FPGA加速器面临着满足更高性能需求的挑战.
- 工业自动化需要更快,更准确的物体检测系统.
研究的目的:
- 为YOLOv5模型介绍一种基于4位量子化的神经网络加速器.
- 为了增强实时处理能力并保持高检测准确度.
- 提高工业物体检测系统的性能和资源效率.
主要方法:
- 为整个YOLO网络,包括第一层,实施了优化的4位量子化方案.
- 开发了一个统一的数字信号处理器 (DSP) 包装方案.
- 引入了一种新的平价增量树架构来支持量子化策略.
主要成果:
- 在平均平均精度 (mAP) 中仅有1.5%的退化,实现了4位量化.
- 通过拟议的硬件架构,将芯片上的DSP使用量减少50%.
- 在FPGA加速器实现808.6GOPS吞吐量和0.49GOPS/DSP效率为YOLOv5s.
结论:
- 拟议的4位量子化加速器显著提高了YOLOv5用于工业物体检测的性能.
- 新型硬件架构在速度,效率和资源利用方面提供了实质性的改进.
- 这项工作为下一代工业自动化提供了可行的解决方案,需要高性能实时对象检测.
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