Compression of Deep Convolutional Neural Networks for Fast and Low Power\n Mobile Applications

Although the latest high-end smartphone has powerful CPU and GPU, running\ndeeper convolutional neural networks (CNNs) for complex tasks such as ImageNet\nclassification on mobile devices is challenging. To deploy deep CNNs on mobile\ndevices, we present a simple and effective scheme to compress the entire CNN,\nwhich we call one-shot whole network compression. The proposed scheme consists\nof three steps: (1) rank selection with variational Bayesian matrix\nfactorization, (2) Tucker decomposition on kernel tensor, and (3) fine-tuning\nto recover accumulated loss of accuracy, and each step can be easily\nimplemented using publicly available tools. We demonstrate the effectiveness of\nthe proposed scheme by…