By using the PYNQ-z2 development platform to accelerate the YOLOv2-Tiny CNN, we achieved target object detection and recognition. Compared with CPU (i7-10710U), the processing capacity was 2.94 times that of CPU, and the power consumption was 3.1% of CPU.Yixuan Zhao...
A Demo for accelerating YOLOv2 in Xilinx's FPGA PYNQ-z2, Zedboard, ZU3EG and ZCU102I have graduated from Jiangnan University, China in July 1, 2019. Related papers are available now. Master thesis"Research of Scalability on FPGA-based Neural Network Accelerator" ...
By using the PYNQ-z2 development platform to accelerate the YOLOv2-Tiny CNN, we achieved target object detection and recognition. Compared with CPU (i7-10710U), the processing capacity was 2.94 times that of CPU, and the power consumption was 3.1% of CPU. 展开 ...
For PYNQ-z2 and Zedboard, in addition to final Linux application( For PYNQ, turn to PYNQ directory; For Zedboard and ZCU102, turn to SDK and PetaLinux), other steps are almost same: (1)Software Simulation Firstly, you should download the darknet source fromhttps://github.com/pjreddie/dark...
基于PYNQ-Z2复现Yolo_v2 参考资料:源项目工程 开发板配置 0 使用说明 0.1 简介 本文档主要分为三个部分: [1] 搭建HLS工程生成Yolo_v2的IP。 [2] 在Vivado中使用生成好的IP进行block design,导出bit文件和tcl文件。 [3] 将相关文件导入至PYNQ-Z2板中,在Jupyter Notebook上进行编程实现。 0.2 所需硬件 ...