  Printed Electronics (PE) feature distinct and remarkable characteristics that
make them a prominent technology for achieving true ubiquitous computing. This
is particularly relevant in application domains that require conformal and
ultra-low cost solutions, which have experienced limited penetration of
computing until now. Unlike silicon-based technologies, PE offer unparalleled
features such as non-recurring engineering costs, ultra-low manufacturing cost,
and on-demand fabrication of conformal, flexible, non-toxic, and stretchable
hardware. However, PE face certain limitations due to their large feature
sizes, that impede the realization of complex circuits, such as machine
learning classifiers. In this work, we address these limitations by leveraging
the principles of Approximate Computing and Bespoke (fully-customized) design.
We propose an automated framework for designing ultra-low power Multilayer
Perceptron (MLP) classifiers which employs, for the first time, a holistic
approach to approximate all functions of the MLP's neurons: multiplication,
accumulation, and activation. Through comprehensive evaluation across various
MLPs of varying size, our framework demonstrates the ability to enable
battery-powered operation of even the most intricate MLP architecture examined,
significantly surpassing the current state of the art.
