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:许多应用领域对灵活性、可伸缩性和孔隙性的需求通常不能通过硅VLSI技术来满足。印刷电子系统(PE)被引入为候选解决方案,可以满足这些要求,并且能够以超低成本在现场和按需制造的超低成本功能整合消费品智能设备。然而,PE的庞大特征限制了这些努力,并由于面积和功率限制而禁止设计复杂的ML电路。虽然分类主要是印刷应用程序的核心任务。在这项工作中,我们首次结合电路实施,审查了神经最小化技术对印刷多层受访者分类师的地区效率的影响。结果显示,最多可达到5%的精度损失可达到8x区域缩小。