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.

中文翻译：

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基于Ti ₃ C ₂ -MXene的忆阻性神经形态系统实现的无监督学习

神经形态硬件系统已经成为未来计算体系结构的有希望的候选者，因为它能够以低能耗和低面积消耗实现自适应学习。但是，对无监督学习的硬件实现仍未深入研究。在这项工作中，我们设计了一个基于忆阻器的硬件系统来实现均值漂移（一种无监督的学习算法）。基于Ti ₃ C ₂ -MXene的忆阻器的交叉开关阵列用于执行乘积运算和电导训练。在具有设备属性的仿真中，基于均值漂移算法的目标跟踪已成功证明，其精度与基于软件的结果相当。这项工作提供了一种通过忆阻性神经形态系统实现无监督学习的方法。