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 R(3)Det 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.