Acceleration of Rotated Object Detection on FPGA

In this brief, an FPGA-based solution is proposed to show the computing efficiency on rotated object detection based on R³Det algorithm. The key idea of our approach is firstly to design reconfigurable neural processing units (NPU) for convolutional neural networks (CNN) and a specific architecture for spatial operations, and then to adopt a novel scheduling scheme to deal with the data dependency on these modules. When implemented on cost-effective Kintex Ultrascale+ XCKU15P FPGA, the proposed solution achieves nearly 3.20 times energy efficiency compared to NVIDIA V100 GPU. The proposed reconfigurable NPU achieves 73.20% DSP efficiency, 30.39% improvement compared to the 56.14% DSP efficiency of the state-of-the-art CNN accelerator on ResNet-50.