Deep learning has become widely used in complex AI applications. Yet, training a deep
neural network (DNNs) model requires a considerable amount of calculations, long running …

Modern AI practices all strive towards the same goal: better results. In the context of deep
learning, the term" results" often refers to the achieved accuracy on a competitive problem …

As machine learning and AI continue to rapidly develop, and with the ever-closer end of
Moore's law, new avenues and novel ideas in architecture design are being created and …

Deep neural networks (DNNs) have been proving the effectiveness in various computing
fields. To provide more efficient computing platforms for DNN applications, it is essential to …

In recent years, Deep Neural Networks (DNNs) have achieved tremendous success for
diverse problems such as classification and decision making. Efficient support for DNNs on …

EdgeAI (Edge computing based Artificial Intelligence) has been most actively researched for
the last few years to handle variety of massively distributed AI applications to meet up the …

With the rise of artificial intelligence in recent years, Deep Neural Networks (DNNs) have
been widely used in many domains. To achieve high performance and energy efficiency …

The execution of AI models, especially deep learning models, involves large quantity of
processor operations and high memory transfer, which is critical to edge environment. To …

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI)
applications including computer vision, speech recognition, and robotics. While DNNs …

Convolutional Neural Networks (CNNs) have begun to permeate all corners of electronic
society (from voice recognition to scene generation) due to their high accuracy and machine …