CS-CNN: Enabling Robust and Efficient Convolutional Neural Networks Inference for Internet-of-Things Applications

Abstract

Recent advances have shown that convolutional neural networks (CNNs) perform excellent in the tasks of image classification and face recognition when the size of data sets is sufficiently large, i.e., over hundreds of thousands training images. Nevertheless, when public data sets are not suitable for training the model for new application scenarios, it is painful to obtain sufficient training examples, especially when the samples have to be labeled manually. Besides, training and inference using CNNs requires significant resources of energy, computation, and memory usage. Therefore, implanting deep CNN models trained and executed on high performance GPU clusters to resource constrained devices, i.e., Internet of Things (IoT) devices, which have permeated into every aspect of modern life, is not appropriate and impractical. Compression technology is an important and popularly used tool to accelerate the training and inference of the CNN models. In this paper, we aim for a step forward in this area: we propose a new compressed CNN model termed CS-CNN for image classification by incorporating the theory of compressive sensing at the input layer of the CNN models to both reduce the resources consumption (evaluated as computation time in this paper) and a required number of training samples. According to our extensive evaluations on the multiple public data sets for deep learning tasks, e.g., MINST and CIFAR-10, using different metrics, we illustrate that the CS-CNN is able to speed up the process of training and inference by a factor of magnitude. Meanwhile, it achieves higher classification accuracy compared with the traditional large CNN models when the size of training database is small.