This paper aims to provide a comprehensive review of the current state of the art at the intersection of deep learning and edge computing. Specifically, it will provide an overview of applications where deep learning is used at the network edge, discuss various approaches for quickly executing ...
Various researchers have used different deep learning networks like convolutional neural networks, auto-encoders, etc., with the edge computing, and showed how the latency and the bandwidth required can be minimized in IoT applications. In this paper, we present a comprehensive study of deep ...
各位老师打扰了,由电子科大万少华老师和河海大学巫义锐老师共同组织的专刊“Deep Learning and Edge Computing for Internet of Things”已经在Applied Sciences-Basel(中科院3区,SCI,IF:2.838)上线。专刊关注人工智能与边缘计算结合后的理论,技术与应用研究。欢迎各位老师同学不吝赐稿。论文截止日期:2023年2月20日。专刊网...
VI-D Performance Evaluation for Edge DL VII Deep Learning Training at Edge VII-A Distributed Training at Edge VII-B Vanilla Federated Learning at Edge VII-C redCommunication-efficient FL VII-D Resource-optimized FL VII-E redSecurity-enhanced FL VIII Deep Learning for Optimizing Edge VIII-A red...
Internet of Vehicles (IoV) are fast becoming the norm in our society, but such a trend also comes with its own set of challenges (e.g., new security and privacy risks due to the expanded attack vectors). In this work, we propose an edge computing-based secure, efficient, and intelligen...
A simple preprocessing step is used to resize the images in the dataset to fit into the shape of the input layer of the network. In this section, first, transfer learning and fine-tuning-based methods and the CNN architectures that are used will be specified. Then, methods with novel CNN...
This article also draws a pipeline of DTL over Edge Computing as a future scope to assist the mitigation of any pandemic.Previous article in issue Next article in issue Keywords AI for Good COVID-19 Deep Learning Edge Computing Pandemic Review Transfer Learning...
written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras deep learning library allows for easy and ...
Deep Learning With Edge Computing: A Review. Proc. IEEE 2019, 107, 1655–1674. [Google Scholar] [CrossRef] Haensch, W.; Gokmen, T.; Puri, R. The Next Generation of Deep Learning Hardware: Analog Computing. Proc. IEEE 2019, 107, 108–122. [Google Scholar] [CrossRef] Biswal, A.; ...
In the context of edge computing (EC) paradigm the new type of specific System on a Chip (SoC) devices with tensor processing architectures (TPAs) appeared for running deep learning (DL) models efficiently on edge computing accelerators (ECAs). Despite availability of numerous benchmarks of ECA...