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Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits

Balaskas, Konstantinos ORCID iD icon 1; Zervakis, Georgios 1; Siozios, Kostas; Tahoori, Mehdi B. 1; Henkel, Jörg 1
1 Institut für Technische Informatik (ITEC), Karlsruher Institut für Technologie (KIT)

Abstract:

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.


Zugehörige Institution(en) am KIT Institut für Technische Informatik (ITEC)
Publikationstyp Forschungsbericht/Preprint
Publikationsdatum 06.04.2022
Sprache Englisch
Identifikator KITopen-ID: 1000148986
Nachgewiesen in arXiv
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