论文信息 - KPynq: A Work-Efficient Triangle-Inequality Based K-Means on FPGA

KPynq: A Work-Efficient Triangle-Inequality Based K-Means on FPGA

K-means is a popular but computation-intensive algorithm for unsupervised learning. To address this issue, we present KPynq, a work-efficient triangle-inequality based K-means on FPGA for handling large-size, high-dimension datasets. KPynq leverages an algorithm-level optimization to balance the performance and computation irregularity, and a hardware architecture design to fully exploit the pipeline and parallel processing capability of various FPGAs. In the experiment, KPynq consistently outperforms the CPU-based standard K-means in terms of its speedup (up to 4.2x) and significant energy efficiency (up to 218x).

Lei Deng | Yufei Ding | Boyuan Feng | Zhaorui Zeng | Yuke Wang

[1] Abel G. Silva-Filho,et al. Hyperspectral images clustering on reconfigurable hardware using the k-means algorithm , 2003, 16th Symposium on Integrated Circuits and Systems Design, 2003. SBCCI 2003. Proceedings..

[2] A. Asuncion,et al. UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[3] Huseyin Seker,et al. FPGA implementation of K-means algorithm for bioinformatics application: An accelerated approach to clustering Microarray data , 2011, 2011 NASA/ESA Conference on Adaptive Hardware and Systems (AHS).