Unsupervised Learning Implemented by Ti3C2-MXene-Based Memristive Neuromorphic System

The neuromorphic hardware system has been a promising candidate for future computing architectures, as it enables adaptive learning at low energy and area consumption. However, hardware implementation of unsupervised learning is still not well-studied. In this work, we design a memristor-based hardware system to realize mean-shift (an unsupervised learning algorithm). A crossbar array of Ti3C2-MXene-based memristors is used to perform a multiply accumulation operation and conductance training. In simulations with device properties, mean-shift-algorithm-based target tracking is successfully demonstrated with comparable accuracy to the software-based result. This work provides an approach to realize unsupervised learning with a memristive neuromorphic system.