AutoPNN (Automated Printed Neural Network via Software-Hardware Approximation and Codesign for Machine Learning Classification in Printed Electronics) is a 2-year research project targeting the development of a fully automated framework for enabling printed self-powered classification circuits.
Printed electronics is increasingly recognized as a key enabler for the Internet of Things
(IoT) as part of the “Fourth Industrial Revolution”. Ultimately, printed electronics form the key technology to
outreach the limited and physically bounded potential of silicon-based IoT systems and realize a “true”
pervasive computing to its full potential, thus fulfilling its deepest promise. Printed electronics is expected to
grow over the next years into a key technology with an enormous economic and social impact. Quoting the
European Commission report on Printing Electronics : for European businesses, flexible and printed
electronics offer opportunities in high value-added products, with most potential in several specific
application areas. These application areas are healthcare and medical devices, smart packaging and logistics,
sensors for IoT, industry and environmental monitoring, and automotive. The fact that in such applications
the core task is mainly classification highlights the utmost impact of our project, i.e., automated generation
of high-accuracy, battery-powered printed ML classifiers. Moreover, considering that partnerships and
collaboration platforms play a critical role to enable European players to stay competitive and develop the
market for flexible electronics in Europe , our objective to open-source AutoPNN is underlined. Finally, we
must note that startups play a central role in the flexible and printed electronics ecosystem due to the fact
that innovation in this field is very risky and it is usually not targeted by larger companies.
The low equipment cost of printed electronics and open-sourcing AutoPNN will enable start-ups to do business on
printed ML classification.