Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits
This work addresses the problem of enabling machine learning on low-cost, flexible printed electronics for applications inaccessible to silicon-based systems, representing an incremental advancement in hardware-efficient ML design.
The authors tackled the challenge of implementing machine learning classifiers on tiny printed circuits by exploiting the hardware-friendly nature of decision trees and using approximate design to create classifiers suitable for ultra-resource constrained applications, resulting in approximate ML classifiers that are feasible for printed electronics.
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.