AI-EDGE

$20M NSF AI-EDGE INSTITUTE AIMS TO TRANSFORM 5G AND BEYOND NETWORKS

Next-generation networks, including 6G and beyond, are changing how and where AI applications are trained and implemented. By providing much higher bandwidth, faster speed, lower latency, and broader coverage, next-generation networks invite a new way of configuring networks. This is especially true at the edges of where most of the growth of network intelligence and distributed AI is expected. These edge networks will encompass mobile and stationary end devices, wireless and wired access points, and computing and sensing devices.

Professors Mingyan Liu and Lei Ying are core members of a newly-established $20M NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE), led by The Ohio State University (OSU). AI-EDGE is expected to make AI more efficient, interactive, and secure for applications in sectors such as intelligent transportation, remote health care, distributed robotics and smart aerospace. “The Michigan team will be developing theories and algorithms for AI-aware networks that deliver the right information at the right time and place to support distributed AI in dynamic, heterogeneous, and non-stationary wireless edge networks,” said Liu. “We will also co-direct a substantial effort in education and workforce development aimed at the joint education in AI and networks.” “Our interest is to design new network architecture and algorithms that can support future AI applications and distributed intelligence,” said Prof. Lei Ying, who will lead the research team focusing on Network Operation for Distributed AI-Applications. Next-generation networks, including 6G and beyond, are changing how and where AI applications are trained and implemented. By providing much higher bandwidth, faster speed, lower latency, and broader coverage, next-generation networks invite a new way of configuring networks. This is especially true at the edges of where most of the growth of network intelligence and distributed AI is expected. These edge networks will encompass mobile and stationary end devices, wireless and wired access points, and computing and sensing devices. “In the future, we envision we can move the intelligence from a centralized cloud center to distributed network edges,” said Ying. The primary benefits of doing this are to satisfy the requirements of real-time applications, such as autonomous driving, and to enhance data security and privacy. Another major challenge is to design a flexible network able to adapt to uncertain and changing conditions. While it may sound like this network would require massive resources, in fact they must also be able to service devices which are severely energy constrained, such as battery-powered IoT devices. Ying’s group has been doing related research looking at distributed computing in networks and how to do dynamic resource allocation in communication networks, but the scale has been smaller and the tasks relatively simple.

AI-EDGE is expected to create a research, education, knowledge transfer and workforce development environment that will help establish U.S. leadership in next-generation edge networks and distributed AI for many decades to come.

Mingyan Liu and Lei Ying are core members of the new $20M NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE)

Ying believes that by being part of this Institute, with multiple groups working together in the same or complementary research topics, he and others can enable exciting applications to occur more quickly, while making the implementation easier. As a member of several research specialties in AI-EDGE, Liu is looking to examine the implications of distributed AI on data security and privacy in these types of networks, and how to design more resilient distributed algorithms, a subject her group has been studying. Educating the individuals who will power AI-EDGE advances is key to the Institute’s ultimate success. As co-directors of Education & Workplace Development, Liu and OSU Professor Eylem Ekici plan to tackle this goal through a multi-pronged approach that focuses on curriculum development, engagement with industry, and outreach to middle and high school age students. For a successful example of education at scale, Liu points to Michigan’s graduate-level course Computational Data Science and Machine Learning. This course offers individualized attention while leveraging technology and teamwork to enable students from a wide variety of majors and backgrounds to succeed. In addition, Michigan ECE’s online learning program known as Continuum is designed to reach thousands of learners from high school to seasoned managers looking to expand into new areas of technology. Equity-centered engineering is central to Michigan Engineering’s approach to education, and will be the guiding principle behind the development of AI-assisted teaching strategies that learn and adapt to individual student progress and engagement in AI-EDGE.

Hands-on learning in and out of the classroom combined with industrial collaborations is a focus of the ECE approach to student professional development, and will be central to a newly-developed curriculum focused on AI and edge networks. Finally, Michigan’s team will work with other members of the Institute to help build excitement for engineering among middle and high school students through special online and in-person programming, such as the Electrify Tech Camp. AI-EDGE is directed by The Ohio State University professor Ness Shroff, Ohio Eminent Scholar in Networking and Communications. In addition to Michigan and OSU, the Institute brings together researchers from Carnegie Mellon University, Northeastern University, Purdue University, University of Wisconsin-Madison, University of Texas-Austin, University of Washington, University of Massachusetts-Amherst, University of Illinois-Urbana-Champaign, and University of Illinois-Chicago, as well as three Department of Defense research labs and four global companies.