Deep learning has delivered its powerfulness in many application domains, especially in
image and speech recognition. As the backbone of deep learning, deep neural networks …

Big Data has fueled the wide deployment of Deep Learning (DL) in many fields, such as
image classification, voice recognition and NLP. The growing number of open source DL …

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

Deep Neural Networks (DNNs) have emerged as a powerful and versatile set of techniques
to address challenging artificial intelligence (AI) problems. Applications in domains such as …

Nowadays, many modern applications, eg autonomous system, and cloud data services
need to capture and process a big amount of raw data at runtime that ultimately necessitates …

Due to the advent of modern embedded systems and mobile devices with constrained
resources, there is a great demand for incredibly efficient deep neural networks for machine …

Deep Neural Networks (DNNs) have been adopted in many systems because of their higher
classification accuracy, with custom hardware implementations great candidates for high …

The recent popularity of deep neural networks (DNNs) has generated considerable research
interest in performing DNN-related computation efficiently. However, the primary focus is …

Deep Neural Networks (DNNs) have become promising solutions for data analysis
especially for raw data processing from sensors. However, using DNN-based approaches …

Deep learning is rapidly becoming a go-to tool for many artificial intelligence problems due
to its ability to outperform other approaches and even humans at many problems. Despite its …