In recent years, advancements in the immense processing ability and parallelism of modern graphics processing units (GPUs) has generated the rapid development of deep learning based on convolutional neural networks (CNN), leading to effective solutions for a variety of issues in artificial intelligence applications. However, the massive amounts of data involved in vision processing limit the application of CNNs to those portable, power-efficient, computation-efficient hardware to process data on site.