HDCluster: An Accurate Clustering Using Brain-Inspired High-Dimensional Computing
Mohsen Imania, Yeseong Kimb, Thomas Worleyc, Saransh Guptad and Tajana Rosinge
Computer Science and Engineering Department, UC San Diego, La Jolla, USA
amoimani@ucsd.edu
byek048@ucsd.edu
ctworley@ucsd.edu
dsgupta@ucsd.edu
etajana@ucsd.edu
ABSTRACT
Internet of things has increased the rate of data generation. Clustering is one of the most important tasks in this domain to find the latent correlation between data. However, performing today’s clustering tasks is often inefficient due to the data movement cost between cores and memory. We propose HDCluster, a brain-inspired unsupervised learning algorithm which clusters input data in a high-dimensional space by fully mapping and processing in memory. Instead of clustering input data in either fixed-point or floating-point representation, HDCluster maps data to vectors with dimension in thousands, called hypervectors, to cluster them. Our evaluation shows that HDCluster provides better clustering quality for the tasks that involve a large amount of data while providing a potential for accelerating in a memory-centric architecture.
Keywords: Hyperdimension computing, Clustering, Braininspired computing.
