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

2021/02/19 by Baohua Sun, Tao Zhang, Sun, Baohua +5 · 1 citation
Computer Science · Neuroscience · Engineering · #Advanced Neural Network Applications #Brain Tumor Detection and Classification #CCD and CMOS Imaging Sensors

paper · pdf · doi:10.48550/arxiv.2103.15756

Abstract

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.

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