Designing hardware accelerators for convolutional neural networks (CNNs) has recently
attracted tremendous attention. Plenty of existing accelerators are built for dense CNNs or …

Convolution neural networks (CNNs) as one of today's main flavor of deep learning
techniques dominate in various image recognition tasks. As the model size of modern CNNs …

The ever-growing parameter size and computation cost of Convolutional Neural Network
(CNN) models hinder their deployment onto resource-constrained platforms. Network …

Convolutional Neural Networks (CNNs) have been widely used in machine learning tasks.
While delivering state-of-the-art accuracy, CNNs are known as both compute-and memory …

Deep neural networks have been shown to outperform prior state-of-the-art solutions that
often relied heavily on hand-engineered feature extraction techniques coupled with simple …

The co-existence of activation sparsity and model sparsity in convolutional neural network
(CNN) models makes sparsity-aware CNN hardware designs very attractive. The existing …

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 …

In recent years, a variety of accelerators on FPGAs have been proposed to speed up the
convolutional neural network (CNN) in many domain-specific application fields. Besides …

Many convolutional neural network (CNN) accelerators are proposed to exploit the sparsity
of the networks recently to enjoy the benefits of both computation and memory reduction …

Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for
machine learning. High performance and extreme energy efficiency are critical for …