  Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware due
to its additive manufacturing process, enabling machine learning (ML)
applications for domains that feature ultra-low cost, conformity, and
non-toxicity requirements that silicon-based systems cannot deliver.
Nevertheless, large feature sizes in PE prohibit the realization of complex
printed ML circuits. In this work, we present, for the first time, an automated
printed-aware software/hardware co-design framework that exploits approximate
computing principles to enable ultra-resource constrained printed multilayer
perceptrons (MLPs). Our evaluation demonstrates that, compared to the
state-of-the-art baseline, our circuits feature on average 6x (5.7x) lower area
(power) and less than 1% accuracy loss.
