Ubiquitous on-device artificial intelligence (AI) is the next step in transforming the myriad of
mobile computing devices in our everyday lives into a new class of truly “smart” devices capable
of constantly observing, learning, and adapting to their environment. Through advances in AI
technology, these intelligent devices will provide proactive assistance and enable new
applications, as well as making our lives safer and the world around us more energy efficient.

Present-day AI features, such as voice-based user interfaces on smartphones, often rely on a
connection to the cloud. In contrast, on-device AI promises to increase the energy efficiency,
privacy, responsiveness, and autonomy of embedded and edge devices by severing their tether
to the cloud. The 3rd on-device intelligence workshop aims to advance the state-of-the-art by
bringing together researchers and practitioners to discuss the key problems, disseminate new
research results, and provide practical tutorial material. Due to the multidisciplinary nature of
on-device AI, collaboration across the traditional computing stack is crucial.

We aim to bring together experts to discuss solutions to the following key challenges:
(1) How do we design, train and optimize ML models tailored to fit a plethora of edge
devices with constrained compute, storage and energy budgets?
(2) How can we ensure privacy and security in ways that are interpretable to users?
(3) How should mobile computing hardware evolve to support the increasing prevalence of
on-device AI workloads?
(4) How can industry and academia collaboratively develop standards and benchmarks to
stimulate the development of an on-device AI research ecosystem?


The workshop agenda includes keynote talks, invited talks, paper talks, panel discussion, and a
poster session. The keynote and invited presenters include prominent leaders in a range of
edge AI subfields. Community building and networking will be interspersed throughout to
facilitate future collaboration across experts in algorithms, software, and hardware engineering
domains.