  With the rise of Deep Learning (DL), our world braces for AI in every edge
device, creating an urgent need for edge-AI SoCs. This SoC hardware needs to
support high throughput, reliable and secure AI processing at Ultra Low Power
(ULP), with a very short time to market. With its strong legacy in edge
solutions and open processing platforms, the EU is well-positioned to become a
leader in this SoC market. However, this requires AI edge processing to become
at least 100 times more energy-efficient, while offering sufficient flexibility
and scalability to deal with AI as a fast-moving target. Since the design space
of these complex SoCs is huge, advanced tooling is needed to make their design
tractable. The CONVOLVE project (currently in Inital stage) addresses these
roadblocks. It takes a holistic approach with innovations at all levels of the
design hierarchy. Starting with an overview of SOTA DL processing support and
our project methodology, this paper presents 8 important design choices largely
impacting the energy efficiency and flexibility of DL hardware. Finding good
solutions is key to making smart-edge computing a reality.
