  The Internet of Things (IoT) and edge computing applications aim to support a
variety of societal needs, including the global pandemic situation that the
entire world is currently experiencing and responses to natural disasters.
  The need for real-time interactive applications such as immersive video
conferencing, augmented/virtual reality, and autonomous vehicles, in education,
healthcare, disaster recovery and other domains, has never been higher. At the
same time, there have been recent technological breakthroughs in highly
relevant fields such as artificial intelligence (AI)/machine learning (ML),
advanced communication systems (5G and beyond), privacy-preserving
computations, and hardware accelerators. 5G mobile communication networks
increase communication capacity, reduce transmission latency and error, and
save energy -- capabilities that are essential for new applications. The
envisioned future 6G technology will integrate many more technologies,
including for example visible light communication, to support groundbreaking
applications, such as holographic communications and high precision
manufacturing. Many of these applications require computations and analytics
close to application end-points: that is, at the edge of the network, rather
than in a centralized cloud. AI techniques applied at the edge have tremendous
potential both to power new applications and to need more efficient operation
of edge infrastructure. However, it is critical to understand where to deploy
AI systems within complex ecosystems consisting of advanced applications and
the specific real-time requirements towards AI systems.
