On-sensor Printed Machine Learning Classification via Bespoke ADC and
  Decision Tree Co-Design
Giorgos Armeniakos Paula L. Duarte Priyanjana Pal Georgios Zervakis Mehdi B. Tahoori Dimitrios Soudris
Printed electronics (PE) technology provides cost-effective hardware with
unmet customization, due to their low non-recurring engineering and fabrication
costs. PE exhibit features such as flexibility, stretchability, porosity, and
conformality, which make them a prominent candidate for enabling ubiquitous
computing. Still, the large feature sizes in PE limit the realization of
complex printed circuits, such as machine learning classifiers, especially when
processing sensor inputs is necessary, mainly due to the costly
analog-to-digital converters (ADCs). To this end, we propose the design of
fully customized ADCs and present, for the first time, a co-design framework
for generating bespoke Decision Tree classifiers. Our comprehensive evaluation
shows that our co-design enables self-powered operation of on-sensor printed
classifiers in all benchmark cases.