In recent years, the continuous development of artificial intelligence has largely been driven
by algorithms and computing power. This paper mainly discusses the training and inference …

The advancement in deep learning has taken the technology world by storm in the last
decade. Although, there is enormous progress made in terms of algorithm performance, the …

The deployment of Convolutional Neural Networks (CNNs) on resource-constrained edge
devices for inference is challenging due to its computation, memory, energy, and bandwidth …

Traffic-Sign Classification is a major task in self-driving cars as well as modern driving
assisting systems can be deployed as an inference engine on the Field Programmable Gate …

Many enterprises are adopting deep learning algorithms in their everyday tasks faster than
ever. Convolutional Neural Networks (CNNs) in particular are being used widely due to the …

Edge computing is a computing framework that offers fewer computing resources compared
to cloud computing but brings enterprise applications closer to data sources like Internet of …

It is clearly known that deep learning applications are enormously used in the image
classification, object tracking and related image analysis techniques. But deep learning …

Convolutional neural networks (CNNs) have dominated a range of applications, from
advanced manufacturing to autonomous cars. For energy cost-efficiency, developing low …

Convolutional neural network plays a prominent role in computer vision applications such as
advanced driver assistance systems which demand high accuracy and low latency. The …

Image classification, speech processing, autonomous driving, and medical diagnosis have
made the adoption of Deep Neural Networks (DNN) mainstream. Many deep networks such …