Convolutional neural networks (CNNs) based deep learning algorithms require high data
flow and computational intensity. For real-time industrial applications, they need to …

In limited-resource edge computing circumstances such as on mobile devices, IoT devices,
and electric vehicles, the energy-efficient optimized convolutional neural network (CNN) …

Recent breakthroughs in the deep convolutional neural networks (CNNs) have led to great
improvements in the accuracy of both vision and auditory systems. Characterized by their …

Deep convolutional neural networks (CNNs) have gained great success in various computer
vision applications. State-of-the-art CNN models for large-scale applications are …

Convolutional neural networks (CNNs) have dominated image recognition and object
detection models in the last few years. They can achieve the highest accuracies with several …

Accelerating deep learning networks in edge computing based on power-efficient and highly
parallel FPGA platforms is an important goal. Combined with deep learning theory, an …

A pre-trained convolutional deep neural network (CNN) is the feed-forward computation
perspective, and it is widely used for the embedded vision systems. One of the applications …

In recent years, dedicated hardware accelerators for the acceleration of the convolutional
neural network (CNN) have been extensively studied. Although many studies have …

Since convolutional computation in deep learning is a large and time-consuming
computation, researchers often use GPU or FPGA to accelerate these computation. This …

Convolutional neural network (CNN) has been widely used in image processing fields.
Object detection models based on CNN, such as YOLO and SSD, have been proved to be …