Memristor-based neuromorphic networks have been actively studied as a promising candidate to overcome the von-Neumann bottleneck in future computing applications. Several recent studies have demonstrated memristor network's capability to perform supervised as well as unsupervised learning, where features inherent in the input are identified and analyzed by comparing with features stored in the memristor network. However, even though in some cases the stored feature vectors can be normalized so that the winning neurons can be directly found by the (input) vector-(stored) vector dot-products, in many other cases, normalization of the feature vectors is not trivial or practically feasible, and calculation of the actual Euclidean distance between the input vector and the stored vector is required. Here we report experimental implementation of memristor crossbar hardware systems that can allow direct comparison of the Euclidean distances without normalizing the weights. The experimental system enables unsupervised K-means clustering algorithm through online learning, and produces high classification accuracy (93.3%) for the standard IRIS data set. The approaches and devices can be used in other unsupervised learning systems, and significantly broaden the range of problems a memristor-based network can solve.
Keywords: Euclidean distance; RRAM; Ta2O5; Unsupervised learning; analog switching; neuromorphic computing.