Enabling Printed Multilayer Perceptrons Realization via Area-Aware Neural Minimization

Abstract

Printed Electronics (PE) set up a new path for the realization of ultra low-cost circuits that can be deployed in every-day consumer goods and disposables. In addition, PE satisfy requirements such as porosity, flexibility, and conformity. However, the large feature sizes in PE and limited device counts incur high restrictions and increased area and power overheads, prohibiting the realization of complex circuits. As a result, although printed Machine Learning (ML) circuits could open new horizons and bring “intelligence” in such domains, the implementation of complex classifiers, as required in target applications, is hardly feasible. In this paper, we aim to address this and focus on the design of battery-powered printed Multilayer Perceptrons (MLPs). To that end, we exploit fully-customized circuit (bespoke) implementations, enabled in PE, and propose a hardware-aware neural minimization framework dedicated for such customized MLP circuits. Our evaluation demonstrates that, for up to 3% accuracy loss, our co-design methodology enables, for the first time, battery-powered operation of complex printed MLPs.