Artificial Intelligence at the Edge: How machine learning at the edge is creating a computing continuum
Abstract:
The number of network-connected devices (sensors, actuators, instruments, computers, and data stores) now substantially exceeds the number of humans on this planet. Billions of things that sense, think, and act are connected to a planet-spanning network of cloud and high-performance computing centres that contain more computers than the entire Internet did just a few years ago.
Parallel computation and machine learning are providing the basis for a new computing continuum that analyses data in-situ, and uses high-performance computing to model, predict, and learn.
This new paradigm is giving rise to intelligent cities, smart agriculture, and advanced manufacturing. The Waggle Platform, developed at Argonne National Laboratory, is an example of a computing system designed to support edge computing and in-situ analytics.
The Array of Things project at the University of Chicago is deploying hundreds of Waggle-based nodes with advanced wireless sensors in Chicago and other cities. Each of the nodes support parallel edge computing, computer vision and machine learning.
Speaker bio:
Dr Pete Beckman is Co-Director of the Northwestern Argonne Institute of Science and Engineering, Argonne National Laboratory, Northwestern University.
Dr Beckman is a recognised expert in high-end computing systems. During the past 25 years, his research has focused on software and architectures for large-scale parallel and distributed computing systems.
For the US Department of Energy's Exascale Computing Project, Dr Beckman leads the Argonne team focused on extreme-scale operating systems and run-time software.
He is the founder and leader of the Waggle project for smart sensors and edge computing that is used by the Array of Things project.
Dr Beckman also coordinates the collaborative technical research activities in extreme-scale computing between the US Department of Energy and Japan’s ministry of education, science, and technology and helps lead the BDEC (Big Data and Extreme Computing) series of international workshops.
He received his PhD in computer science from Indiana University.