Intelligent Edge: When Wireless Network Meets Machine Learning

Over the past years, the central role of the Internet in modern society has created challenges of efficiency and flexibility, especially as usage intensifies due to proliferation of wireless-network-enabled mobile devices at the edge of the Internet. In the meanwhile, machine learning techniques, such as deep learning and reinforcement learning, have been applied to a variety of different applications to successfully tackle their problems. In an era when wireless network meets machine learning, our vision is that they will reinforce each other, ultimately leading to an intelligent edge in the Internet. In the envisioned intelligent edge, machine learning could be utilized to further improve the quality of the services provided to mobile devices. Current mobile devices tend to have limited computation resources and constrained battery capacity. They often need to offload computationally intensive tasks to high-performance servers in order to complete the tasks in a timely and energy-efficient manner. Mobile Edge Computing (MEC) is proposed to solve the offloading problem by placing modest-performance edge servers at locations close to mobile devices (e.g. cellular base stations). Despite the advantage of MEC, a series of technical challenges need to be tackled before it can be widely deployed. Machine learning has been used to improve the performance of MEC. We believe that machine learning will be further utilized in MEC, helping provide satisfactory services for mobile devices. Furthermore, mobile devices in the intelligent edge could contribute to the realization of usable machine learning. Technically, the training and inference phases of many machine learning techniques require a large amount of computational resources. The creation of AlphaGo, the first computer program that beats a professional Go player, is a key milestone in the recent revival of machine learning. However, the early version of AlphaGo needs to use 176 GPUs to reach its best performance. The amount of computational resources required by machine learning seriously hinders its real-life application. The existence of abundant mobile devices at the edge of the Internet literally creates an unprecedented opportunity for mobile-device-based distributed machine learning. Each mobile device is a natural computing node in distributed machine learning. The computational resources in a collection of distributed mobile devices could significantly help speed up the training and inference process. When wireless network meets machine learning, the edge of the Internet will become "intelligent" because mobile devices would be served in an intelligent manner and wireless network could help realize usable machine learning. In the proposed project, we will explore varied approaches to pave the way for the emergence of the intelligent edge.