标题： 最新成果：基于串行SVM的节能打印机器学习分类器 显示英文标题

标题： Late Breaking Results: Energy-Efficient Printed Machine Learning Classifiers with Sequential SVMs

摘要： 与基于硅的技术相比，印刷电子学（PE）为机器学习（ML）电路提供了一种机械柔性和更具成本效益的解决方案。 然而，由于特征尺寸较大，印刷分类器受到高功耗、面积和能耗开销的限制，这阻碍了电池供电系统的实现。 在这项工作中，我们设计了顺序印刷定制支持向量机（SVM）电路，这些电路符合现有印刷电池的功率约束，同时最小化能耗，从而延长电池寿命。 我们的结果显示，与最先进的技术相比，在保持更高精度的同时实现了6.5倍的能耗节省。 显示英文摘要

摘要： Printed Electronics (PE) provide a mechanically flexible and cost-effective solution for machine learning (ML) circuits, compared to silicon-based technologies. However, due to large feature sizes, printed classifiers are limited by high power, area, and energy overheads, which restricts the realization of battery-powered systems. In this work, we design sequential printed bespoke Support Vector Machine (SVM) circuits that adhere to the power constraints of existing printed batteries while minimizing energy consumption, thereby boosting battery life. Our results show 6.5x energy savings while maintaining higher accuracy compared to the state of the art.