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且显著的特性，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尤为相关，而这些领域至今仍面临计算渗透率有限的问题。与硅基技术不同，印刷电子具备非重复性工程成本、超低制造成本以及按需制造共形、柔性、无毒且可拉伸硬件的独特优势。然而，由于特征尺寸较大，印刷电子在实现复杂电路（例如机器学习分类器）方面面临特定限制。本研究通过运用近似计算与定制化（全定制）设计的原理来解决这些限制。我们提出了一种用于设计超低功耗多层感知器（MLP）分类器的自动化框架，该框架首次采用整体性方法近似MLP神经元的所有功能：乘法、累加和激活。通过对不同规模MLP的综合评估，我们的框架展示了即使对最复杂的MLP架构也能实现电池供电运行的能力，显著超越了当前最先进技术水平。