Convolutional neural network (CNN) is the state-of-the-art deep learning approach
employed in various applications. Real-time CNN implementations in resource limited …

Convolutional neural networks (CNNs) have been widely deployed in the fields of computer
vision and pattern recognition because of their high accuracy. However, large convolution …

Convolutional neural networks (CNN) have been proved to be an effective method in the
field of artificial intelligence (AI), and large-scale deploying CNN to embedded devices, no …

Fueled by ImageNet Large Scale Visual Recognition Challenge and Common Objects in
Context competitions, the convolutional neural network (CNN) has become important in …

Over the past few years, Convolution Neural Networks (CNN) have been extensively
adopted in broad AI applications and have achieved noticeable effect. Deploying the …

Convolutional neural networks (CNNs) are widely used and have achieved great success in
computer vision and speech processing applications. However, deploying the large-scale …

To enable efficient deployment of convolutional neural networks (CNNs) on embedded
platforms for different computer vision applications, several convolution variants have been …

Hardware design of deep convolutional neural networks (CNNs) faces challenges of high
computational complexity and data bandwidth as well as huge divergence in different CNN …

In recent years, convolutional neural network (CNN) based methods have achieved great
success in a large number of applications and have been among the most powerful and …

Currently, deep neural networks (DNNs) are widely used for various applications and have
achieved state-of-the-art performances. A survey about the prior researches addressing …