In recent years, Artificial Intelligence (AI) has been widely deployed
in a variety of business sectors and industries, yielding numbers of
revolutionary applications and services that are primarily driven by
high-performance computation and storage facilities in the cloud. On the
other hand, embedding intelligence into edge devices is highly demanded
by emerging applications such as autonomous systems, human-machine
interactions, and the Internet of Things (IoT). In these applications,
it is advantageous to process data near or at the source of data to
improve energy & spectrum efficiency and security, and decrease
latency. Although the computation capability of edge devices has
increased tremendously during the past decade, it is still challenging
to perform sophisticated AI algorithms in these resource-constrained
edge devices, which calls for not only low-power chips for energy
efficient processing at the edge but also a system-level framework to
distribute resources and tasks along the edge-cloud continuum. In this
overview, we summarize dedicated edge hardware for machine learning from
embedded applications to sub-mW “always-on” IoT nodes. Recent advances
of circuits and systems incorporating joint design of architectures and
algorithms will be reviewed. Fog computing paradigm that enables
processing at the edge while still offering the possibility to interact
with the cloud will be covered, with focus on opportunities and
challenges of exploiting fog computing in AI as a bridge between the
edge device and the cloud.