Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a
wide range of cognitive tasks, and due to this, they have received significant interest from the …

Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater
number of problems, ranging from speech recognition to image classification and …

以在现场可编程门阵列(FPGA) 上部署卷积神经网络为背景, 提出了卷积神经网络在硬件上进行
并行加速的方案. 主要是通过分析卷积神经网络的结构特点, 对数据的存储, 读取 …

针对卷积神经网络计算硬件化实现困难的问题, 之前大部分卷积神经网络加速器的设计都集中于
解决计算性能和带宽瓶颈, 忽视了卷积神经网络稀疏性对加速器设计的重要意义 …

The convolutional neural network (CNN) has become widely used in a variety of vision
recognition applications, and the hardware acceleration of CNN is in urgent need as …

Since the invention of the first programmable CPU (Central Processing Unit), the need for
more capable computing machines has been growing by leaps and bounds. This has …

Abstract Convolutional Neural Networks (CNNs) have applications in many valuable
domains such as object detection for autonomous cars and security using facial recognition …

The progress of deep learning has rapidly accelerated development at an exponential rate
of existing technologies. Deep convolutionary algorithms have received much popularity …

ABSTRACT Nowadays Convolution Neural Network (CNN) has become the state of the art
for machine learning algorithms due to their high accuracy. However, implementation of …

To reduce DMA utilization for multiple algorithm IPs on FPGA, a channel configurable and
multiplex DMA device (CMDMA) is proposed for asynchronous and heterogeneous …