  Printed electronics offer ultra-low manufacturing costs and the potential for
on-demand fabrication of flexible hardware. However, significant intrinsic
constraints stemming from their large feature sizes and low integration density
pose design challenges that hinder their practicality. In this work, we conduct
a holistic exploration of printed neural network accelerators, starting from
the analog-to-digital interface - a major area and power sink for sensor
processing applications - and extending to networks of ternary neurons and
their implementation. We propose bespoke ternary neural networks using
approximate popcount and popcount-compare units, developed through a
multi-phase evolutionary optimization approach and interfaced with sensors via
customizable analog-to-binary converters. Our evaluation results show that the
presented designs outperform the state of the art, achieving at least 6x
improvement in area and 19x in power. To our knowledge, they represent the
first open-source digital printed neural network classifiers capable of
operating with existing printed energy harvesters.
