NeuroSOFM-Classifier: Nanoscale FeFETs Based Low Power Neuromorphic Architecture for Classification Using Continuous Real-Time Unsupervised Clustering

• Published in: IEEE transactions on nanotechnology Vol. 23; pp. 124 - 131
• Main Authors: Barve, Siddharth, Jha, Rashmi
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Classification algorithms; Classifiers; Clustering; CMOS; Computer architecture; Data analysis; Feature maps; FeFETs; ferroelectric devices; Ferroelectricity; Field effect transistors; Logic gates; Machine learning; Neural network hardware; Neuromorphics; Neurons; Real time; Self organizing maps; Semiconductor devices; Supervised learning; Topology; unsupervised learning
• ISSN: 1536-125X; 1941-0085
• DOI: 10.1109/TNANO.2024.3357068

Supervised machine learning techniques are becoming subject of significant interest in data analysis. However, the high memory bandwidth requirement of current implementations, scarcity of labeled data, and dynamic environments in many applications prevent implementation of supervised machine learning techniques. In this work, we propose a neuromorphic architecture implementing the self-organizing feature map algorithm using nanoscale ferroelectric field-effect transistors (FeFETs) and complementary metal-oxide-semiconductor (CMOS) technology to produce a semi-supervised NeuroSOFM-Classifier. A best matching input (BMI) identifier circuit allows for very few labeled samples to be used to provide supervised class labels for each hardware neuron in the NeuroSOFM-Classifier. The NeuroSOFM-Classifier architecture can then be used to inference or classify the new data in real-time. This NeuroSOFM-Classifier, trained on just 2% of the labeled data, is capable of classifying COVID-19 patient chest x-rays with an average accuracy of 83%.