The demand of many application domains for flexibility, stretchability, and porosity cannot be typically met by the silicon VLSI technologies. Printed Electronics (PE) has been introduced as a candidate solution that can satisfy those requirements and enable the integration of smart devices on consumer goods at ultra low-cost enabling also in situ and ondemand fabrication. However, the large features sizes in PE constraint those efforts and prohibit the design of complex ML circuits due to area and power limitations. Though, classification is mainly the core task in printed applications. In this work, we examine, for the first time, the impact of neural minimization techniques, in conjunction with bespoke circuit implementations, on the area-efficiency of printed Multilayer Perceptron classifiers. Results show that for up to 5% accuracy loss up to 8x area reduction can be achieved.

翻译：许多应用领域对柔性、可拉伸性和多孔性的需求通常无法通过硅基VLSI技术实现。印刷电子（PE）作为一种候选解决方案被引入，其既能满足这些要求，又能以超低成本的实现在位按需制造方式将智能器件集成到消费品中。然而，印刷电子中较大的特征尺寸制约了这些努力，并因面积和功耗限制阻碍了复杂ML电路的设计——尽管分类仍是印刷应用中的核心任务。本研究首次探讨了神经最小化技术结合定制电路实现方法对印刷多层感知机分类器面积效率的影响。结果表明，在精度损失不超过5%的情况下，可实现高达8倍的面积缩减。