Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …

The design of convolutional neural network (CNN) hardware accelerators based on a single
computing engine (CE) architecture or multi-CE architecture has received widespread …

Convolutional neural networks (CNN) have emerged as a dominant deep learning
technique in various fields, including image processing, computer vision, and intelligent …

In the scenarios of the Internet of Things, fall detection holds increasing significance in the
health monitoring of elderly individuals. While most current research has achieved …

In the evolving landscape of computer vision, the integration of machine learning algorithms
with cutting-edge hardware platforms is increasingly pivotal, especially in the context of …

Skip connections have emerged as a key component of modern convolutional neural
networks (CNNs) for computer vision tasks, allowing for the creation of more accurate and …

Sim-to-real robot learning has been used in various applications, but its implementation in
software may not provide the best performance. This tutorial describes how hardware …

A multitude of accelerators have been designed to accelerate the inference of widely-used
Convolutional Neural Networks (CNNs). They can primarily be classified into two …

Object detection models have demonstrated outstanding performance in terms of accuracy.
However, map** convolutional neural network-based object-detection models to memory …

Nowadays, the deployment of intelligent networks on hardware devices for real-time
applications is gaining popularity in both academia and industry. However, on-chip …