Exploiting Dual-Gate Ambipolar CNFETs for Scalable Machine Learning Classification
This work addresses the need for scalable and efficient hardware for machine learning classification, though it appears incremental as it builds on existing mixed-signal classifier designs using a new transistor technology.
The paper tackled designing a machine learning classifier using ambipolar carbon nanotube field-effect transistors (AP-CNFETs), achieving 90% accuracy on the MNIST dataset with no degradation compared to software, while operating at 250 MHz with lower power and smaller size than CMOS and memristor-based alternatives.
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.