  Object detection is widely used on embedded devices. With the wide
availability of CNN (Convolutional Neural Networks) accelerator chips, the
object detection applications are expected to run with low power consumption,
and high inference speed. In addition, the CPU load is expected to be as low as
possible for a CNN accelerator chip working as a co-processor with a host CPU.
In this paper, we optimize the object detection model on the CNN accelerator
chip by minimizing the CPU load. The resulting model is called GnetDet. The
experimental result shows that the GnetDet model running on a 224mW chip
achieves the speed of 106FPS with excellent accuracy.
