Abstract Deep Neural Network (DNN) models for both resource-rich environments and
resource-constrained devices have become abundant in recent years. As of now, the …

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 …

Object detection is an important area in self-driving automotive. The YOLO algorithm and its
well-embedded implementation is a promising solution for object detection. In this paper, a …

Progress is being made to deploy convolutional neural networks (CNNs) into the Internet of
Things (IoT) edge devices for handling image analysis tasks locally. These tasks require low …

The three-dimensional convolutional neural networks have abundant parameters and
computational costs. It is urgent to compress the three-dimensional convolutional neural …

In recent years, convolutional neural network (CNN)-based object detection algorithms have
made breakthroughs, and much of the research corresponds to hardware accelerator …

Traditional High-Level Synthesis (HLS) provides rapid prototyping of hardware accelerators
without coding with Hardware Description Languages (HDLs). However, such an approach …

Convolutional neural network (CNN) is widely deployed on edge devices, performing tasks
such as objective detection, image recognition and acoustic recognition. However, the …

Deep neural networks (DNNs) have strong fitting ability on a variety of computer vision tasks,
but they also require intensive computing power and large storage space, which are not …

Due to its advantages of low latency, low power consumption, and high flexibility, FPGA-
based acceleration technology has been more and more widely studied and applied in the …