K-MLIO: Enabling K-Means for Large Data-sets and Memory Constrained Embedded Systems

Machine Learning (ML) algorithms are increasingly used in embedded systems to perform different tasks such as clustering and pattern recognition. These algorithms are both compute and memory intensive whilst embedded devices offer lower hardware capabilities as compared to traditional ML platforms. K-means clustering is one of the widely used ML algorithms. In the case of large data-sets, our analysis showed that on average, more than 70% of the execution time is spent on I/Os. In this paper, we present a version of K-means that drastically reduces the number of I/Os by spanning the data-set only once as compared to the traditional version that reads it several times according to the number of iterations performed. Our evaluation showed that the proposed strategy reduces the overall execution time on large data-sets by 60% on average while lowering the number I/Os operations by 90% with a comparable precision to the traditional K-means implementation.