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.

翻译：印刷电子技术具有独特而卓越的特性，使其成为实现真正普适计算的重要技术。这在需要共形和超低成本解决方案的应用领域中尤为相关，这些领域迄今为止计算技术的渗透仍然有限。与硅基技术不同，印刷电子技术提供了无与伦比的特性，如非重复性工程成本、超低制造成本以及按需制造共形、柔性、无毒和可拉伸的硬件。然而，由于其特征尺寸较大，印刷电子技术在实现复杂电路（如机器学习分类器）方面面临一定限制。在本工作中，我们通过利用近似计算和定制化（完全定制）设计原则来解决这些限制。我们提出了一种自动化框架，用于设计超低功耗多层感知器分类器，该框架首次采用整体化方法对MLP神经元的所有功能进行近似：乘法、累加和激活函数。通过对不同规模的多种MLP进行全面评估，我们的框架证明了即使对于所研究的最复杂MLP架构，也能实现电池供电运行，显著超越了当前技术水平。