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：印刷电子技术凭借其独特而显著的特性，成为实现真正普适计算的重要技术，尤其适用于需要共形和超低成本解决方案的应用领域——这些领域至今仍鲜有计算技术的渗透。与硅基技术不同，印刷电子具备无重复工程成本、超低制造成本以及按需制造共形、柔性、无毒、可拉伸硬件等无与伦比的特性。然而，受限于较大特征尺寸，印刷电子在实现机器学习分类器等复杂电路时面临挑战。本研究通过融合近似计算与定制化（完全定制化）设计的原理，首次提出一种采用整体化方法近似处理多层感知器神经元全部功能（乘法、累加及激活）的自动化框架，用于设计超低功耗多层感知器分类器。针对不同规模的多层感知器进行的全面评估表明，本框架能够使最复杂的多层感知器架构实现电池供电运行，显著超越当前最先进技术水平。