Artificial intelligence (AI) and machine learning (ML) tools play a significant role in the recent
evolution of smart systems. AI solutions are pushing towards a significant shift in many fields …

Implementation of convolutional neural networks (CNNs) on resource constrained devices
like FPGA (example: Zynq) etc. is important for intelligence in edge computing. This paper …

In order to meet the information processing requirements that large amount of
heterogeneous input data are in the real-time flight process of aircraft, a neural network is …

For most of the edge-based system-on-chips (SoCs), the inference (eg a CNN accelerator)
and the security subsystems are typically separately designed and interacted with each …

It is well known that there is an increasing interest in edge computing to reduce the distance
between cloud and end devices, especially for machine learning (ML) methods. However …

为解决卷积神经网络在FPGA 平台上进行硬件加速时存在的资源利用率低和资源受限问题,
提出了一种基于FPGA 动态部分重构技术和Winograd 快速卷积的卷积神经网络加速器 …

With the rapid development of convolutional neural networks (CNN), the design of hardware
accelerators for CNN calculations has become a major focus of current research. However …

Edge computing is a computing framework that offers fewer computing resources compared
to cloud computing but brings enterprise applications closer to data sources like Internet of …

Deployment of complex convolutional neural network (CNN) algorithms on Field
Programmable Gate Arrays (FPGAs) is a non-trivial task and it becomes even more …

In a range of fields such as emotion detection, medical image processing and speech
recognition, deep learning has recently achieved good results. With the pursuit of more …