The amount of data processed in the cloud, the development of Internet-of-Things (IoT)
applications, and growing data privacy concerns force the transition from cloud-based to …

In the field of object detection, deep learning has greatly improved accuracy compared to
previous algorithms and has been used widely in recent years. However, object detection …

In computer architecture studies, simulators are crucial for design verification, reducing
research and development time and ensuring the high accuracy of verification results …

In this manuscript, previously trained Convolutional neural network (CNN), Quantum Neural
Network (QNN), and Binarized Neural Network (BNN) models performed employing Tensor …

Deep neural networks executing with low precision at inference time can gain acceleration
and compression advantages over their high-precision counterparts, but need to overcome …

Limited by huge computational cost, high inference latency and large memory consumption,
existing 3D point cloud object detection methods are hard to be deployed on Internet of …

A convolutional neural network-based multiobject detection and tracking algorithm can be
applied to vehicle detection and traffic flow statistics, thus enabling smart transportation …

Deep neural network (DNN)-based object detection has been investigated and applied to
various real-time applications. However, it is hard to employ the DNNs in embedded …

The Vision Transformer (ViT) models have demonstrated excellent performance in computer
vision tasks, but a large amount of computation and memory access for massive matrix …

Inspired by convolutional neural networks, graph convolutional networks (GCNs) have been
proposed for processing non-Euclidean graph data and successfully been applied in …