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
