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Although Printed Electronics (PE) cannot compete with silicon-based systemsin conventional evaluation metrics, e.g., integration density, area andperformance, PE offers attractive properties such as on-demand ultra-low-costfabrication, flexibility and non-toxicity. As a result, it targets applicationdomains that are untouchable by lithography-based silicon electronics and thushave not yet seen much proliferation of computing. However, despite theattractive characteristics of PE, the large feature sizes in PE prohibit therealization of complex printed circuits, such as Machine Learning (ML)classifiers. In this work, we exploit the hardware-friendly nature of DecisionTrees for machine learning classification and leverage the hardware-efficiencyof the approximate design in order to generate approximate ML classifiers thatare suitable for tiny, ultra-resource constrained, and battery-powered printedapplications.

Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits