As the demand for neural network operations on edge devices increases, energy-efficient
neural network inference solutions become necessary. To this end, this paper proposes a …

Convolutional Neural Networks (CNNs) have been widely used in the field of computer
vision. Due to the computational complexity of CNNs, their computational efficiency has …

Convolutional neural network (CNN) and related deep learning algorithms represent the
state-of-art ability in several computer vision tasks, such as image classification and video …

Convolutional neural network (CNN) is widely applied to image recognition with high
recognition accuracy. CNN has a wider implementation in general-purpose processors and …

Image classification finds its suitability in applications ranging from medical diagnostics to
autonomous vehicles. The existing architectures are computationally exhaustive, complex …

With the rapid development of in-depth learning, neural network and deep learning
algorithms have been widely used in various fields, eg, image, video and voice processing …

Convolutional neural networks (CNNs) have emerged as the most efficient technique for
solving a host of machine learning tasks. Compute and memory intensive nature of CNN has …

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 …

Convolutional neural networks (CNN), which have the advantage of automatically detecting
the important features of the input data without any human interfere, are widely used in many …

Convolutional neural network (CNN) has achieved excellent achievements in the field of
image recognition. Because of the better energy efficiency and fewer development cycle …