Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/DATE.2019.8715147 | 2019 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE) |
Keywords | Field | DocType |
Hyperdimension computing, Clustering, Brain-inspired computing | Cluster (physics),Data mining,Architecture,Unsupervised learning algorithm,Computer science,Parallel computing,Internet of Things,Correlation,Cluster analysis,Test data generation | Conference |
ISSN | Citations | PageRank |
1530-1591 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohsen Imani | 1 | 341 | 48.13 |
| Yeseong Kim | 2 | 72 | 8.35 |
| Thomas Worley | 3 | 0 | 0.34 |
| Saransh Gupta | 4 | 101 | 11.58 |
| Tajana Simunic | 5 | 3198 | 266.23 |