1 research outputs found
<i>K</i>‑means Data Clustering with Memristor Networks
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 <i>K</i>-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