Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

The convolutional neural network (CNN) is one of the most used deep learning models for
image detection and classification, due to its high accuracy when compared to other …

Deep neural networks (DNNs) are being prototyped for a variety of artificial intelligence (AI)
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …

Deep learning is now present in a wide range of services and applications, replacing and
complementing other machine learning algorithms. Performing training and inference of …

Nowadays, with the increasing shortage of traditional medical resources, the existing
portable monitoring healthcare device is no longer satisfactory. Thus, wearable healthcare …

Abstract Convolutional Neural Networks (CNNs) have improved several computer vision
applications, like object detection and classification, when compared to other machine …

Convolutional neural networks have become the state of the art of machine learning for a
vast set of applications, especially for image classification and object detection. There are …

Quantization has been proven to be a vital method for improving the inference efficiency of
deep neural networks (DNNs). However, it is still challenging to strike a good balance …

Deep learning models are taking place at many artificial intelligence tasks. These models
are achieving better results but need more computing power and memory. Therefore …

Convolutional Neural Networks (CNN) are quite useful in edge devices for security,
surveillance, and many others. Running CNNs in embedded devices is a design challenge …