利用双栅双极CNFET实现可扩展的机器学习分类
Exploiting Dual-Gate Ambipolar CNFETs for Scalable Machine Learning Classification
摘要：
双极碳纳米管场效应晶体管（AP-CNFET）具有独特的电学特性，如三态操作和双向性，使系统具有复杂和可重构的计算能力。本文利用AP-CNFET设计了一个混合信号机器学习（ML）分类器。该分类器是在SPICE中设计的，特征尺寸为15 nm，工作频率为250 MHz。该系统基于MNIST数字数据集进行了演示，与Python中的该数据集的分类相比，获得了90%的准确率，并且准确率没有下降。该系统还具有较低的功耗和较小的物理尺寸相比，最先进的CMOS和忆阻器为基础的混合信号分类器。
英文摘要：
Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibit unique electrical characteristics, such as tri-state operation and bi-directionality, enabling systems with complex and reconfigurable computing. In this paper, AP-CNFETs are used to design a mixed-signal machine learning (ML) classifier. The classifier is designed in SPICE with feature size of 15 nm and operates at 250 MHz. The system is demonstrated based on MNIST digit dataset, yielding 90% accuracy and no accuracy degradation as compared with the classification of this dataset in Python. The system also exhibits lower power consumption and smaller physical size as compared with the state-of-the-art CMOS and memristor based mixed-signal classifiers.
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