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Object detection is widely used on embedded devices. With the wideavailability of CNN (Convolutional Neural Networks) accelerator chips, theobject 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 aspossible 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 acceleratorchip by minimizing the CPU load. The resulting model is called GnetDet. Theexperimental result shows that the GnetDet model running on a 224mW chipachieves the speed of 106FPS with excellent accuracy.

GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS