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

Convolutional neural networks (CNNs) require numerous computations and external
memory accesses. Frequent accesses to off-chip memory cause slow processing and large …

Convolutional Neural Networks (CNNs) are influencing major breakthroughs in computer
vision by achieving unprecedented accuracy on tasks such as image classification, object …

This paper implements an OpenCL-based FPGA accelerator for YOLOv2 on Arria-10
GX1150 FPGA board. The hardware architecture adopts a scalable pipeline design to …

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 …

With the recent advances in the fields of machine learning, neural networks and deep-
learning algorithms have become a prevalent subject of computer vision. Especially for tasks …

FPGAs have emerged as a promising platform for implementing neural networks due to their
reconfigurability, parallelism, and low power consumption. Nonetheless, designing and …

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

Convolutional neural network (CNN) based object detection algorithms are becoming
dominant in many application fields due to their superior accuracy advantage over …

YOLOv2 is an object detection algorithm grounded on the Darknet neural network, widely
applied in the advanced driver assistance system. Nevertheless, the YOLOv2 algorithm must …