In the 5G intelligent edge scenario, more and more accelerator-based single-board
computers (SBCs) with low power consumption and high performance are being used as …

In recent years, convolutional neural network (CNN)-based methods have been widely used
for optical remote sensing object detection and have shown excellent performance. Some …

Deep Convolutional Neural Network (CNN) algorithm has recently gained popularity in
many applications such as image classification, video analytic and object detection. Being …

FPGAs have been an efficient accelerator for CNN inference due to its high performance,
flexibility, and energy-efficiency. To improve the performance of CNNs on FPGAs, fast …

Convolution neural network is widely used in image recognition because it can imitate the
behavioral characteristics of biological visual nerve, and has high recognition accuracy. It is …

Convolutional neural network (CNN) architectures are generally heavy on memory and
computational requirements which make them infeasible for embedded systems with limited …

In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art results
for many computer vision tasks. However, the traditional CNNs are computational-intensive …

Convolutional neural network (CNN) offers significant accuracy in image detection. To
implement image detection using CNN in the Internet of Things (IoT) devices, a streaming …