  Although Printed Electronics (PE) cannot compete with silicon-based systems
in conventional evaluation metrics, e.g., integration density, area and
performance, PE offers attractive properties such as on-demand ultra-low-cost
fabrication, flexibility and non-toxicity. As a result, it targets application
domains that are untouchable by lithography-based silicon electronics and thus
have not yet seen much proliferation of computing. However, despite the
attractive characteristics of PE, the large feature sizes in PE prohibit the
realization of complex printed circuits, such as Machine Learning (ML)
classifiers. In this work, we exploit the hardware-friendly nature of Decision
Trees for machine learning classification and leverage the hardware-efficiency
of the approximate design in order to generate approximate ML classifiers that
are suitable for tiny, ultra-resource constrained, and battery-powered printed
applications.

Hardware Architecture;Machine Learning