Convolutional Neural Networks (CNNs) are frequently used in a wide range of applications,
including speech, image recognition and natural language processing. However, due to the …

Convolutional neural network (CNN) training is computationally intensive, requiring a great
deal of time and resources. Exploiting data sparsity is a promising method to ac-celerate …

Convolutional Neural Networks (CNNs) have been shown to be very useful in image
recognition and other Artificial Intelligence (AI) applications, however, at the expense of …

Convolutional Neural Networks (CNNs) have been shown to be very useful in image
recognition and other AI applications. CNNs are usually computationally intensive. To …

Convolutional neural networks (CNNs) have achieved great success in performing cognitive
tasks. However, execution of CNNs requires a large amount of computing resources and …

Neural networks have been widely used in a large range of domains, researchers tune
numbers of layrs, neurons and synapses to adapt various applications. As a consequence …

Recently, the layer-wise N: M fine-grained sparse neural network algorithm (ie, every M-
weights contains N non-zero values) has attracted tremendous attention, as it can effectively …

Deep convolutional neural networks (CNNs) have achieved remarkable performance with
the cost of huge computation. Moreover, the current trend of CNNs is towards more complex …

Deep convolutional neural networks (CNNs) have achieved state-of-the-art performance in a
wide range of applications. However, deeper CNN models, which are usually computation …

Convolutional neural networks (CNNs) have become a widely popular method for
performing common computer vision tasks, such as image classification and object …