A Development Platform of Intelligent Mobile APP Based on Edge Computing In this paper, we propose a platform based on the architecture of edge computing for the development of intelligent mobile APP. The proposed platform has a number of advantages as follows. First, it supports static and dynamic visualization for reducing the programming and training effort of deep learning models. Second, it offers a runtime library for mobile APPs to exploit external deep learning models and make use of edge servers for the execution of the models. Third, it provides a friendly user interface to partition a deep learning model and execute the model by different work modes for performance optimization. Forth, it can dynamically redirect the requests of mobile APPs for achieving load balance and minimizing the response time of edge servers.

Toward an Intelligent Edge: Wireless Communication Meets Machine Learning The recent revival of AI is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interest in realizing an intelligent edge to support AIenabled applications at various edge devices. Accordingly, a new research area, called edge learning, has emerged, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design guidelines for wireless communication in edge learning, collectively called learning driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design guidelines. Unique research opportunities are identified.