SoC implementation of depthwise separable convolution for object detection
Depthwise separable convolution has been used in CNN to reduce operations and parameters with only limited loss in accuracy. However, the state of the art CNNs that adopt depthwise separable convolution still require a powerful computing platform such as a Graphics Processing Unit (GPU) In this paper: firstly, we design three kinds of work models for process element(PE) based on FPGA: standard convolution model, depthwise convolution model and pointwise convolution model; then we implement MobileNet SSD on SoC for object detection, and our method requires only limited resources; besides, we propose a method to transform float point data into fixed point data, making computation more convenient. And the results show that our work can detect object at 13 fps.
ICCIP '19