Abstract
In the era of BD explosion, poses significant challenges in the processing speed due to huge data volume and high dimension. To address this problem, we design a hardware implementation of K-means based on FPGA, named SAKMA, which can accelerate the whole algorithm in hardware and can be easily configured via parameters. What’s more, the accelerator makes the data size unlimited and can solve the problem about frequent off-chip memory access in a certain extent. Taking into account the hardware resource and power consumption, the SAKMA architecture adopts novel methods to accelerate the algorithm, including pipeline, tile technique, duplication parallelism, and hardware adder tree structures. In order to evaluate the performance of accelerator, we have constructed a real hardware prototype on Xilinx ZedBoard xc7z020clg484-1 FPGA. Experimental results demonstrate that the SAKMA architecture can achieve the speedup at 20.5 × with the affordable hardware cost.
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References
Wang, C., Li, X., Chen, P., Wang, A., Zhou, X., Yu, H.: Heterogeneous cloud framework for big data genome sequencing. IEEE/ACM Trans. Comput. Biol. Bioinform. 12(1), 166–178 (2015)
Wang, C., Li, X., Zhou, X.: SODA: software defined FPGA based accelerators for big data, pp. 884-887 (2015)
Winterstein, F., Bayliss, S., Constantinides, G.A.: FPGA-based K-means clustering using tree-based data structures. In: 23rd International Conference on Field Programmable Logic and Applications (FPL) 2013, pp. 1–6 (2013)
Macgregor, P.F., Squire, J.A.: Application of microarrays to the analysis of gene expression in cancer. Clin. Chem. 48, 1170–1177 (2002)
Akay, M.: Genomics and Proteomics Engineering in Medicine and Biology (IEEE Press Series in Biomedical Engineering). John Wiley & Sons, New York (2007)
Lavenier, D.: FPGA implementation of the k-means clustering algorithm for hyper spectral images. In: Los Alamos National Laboratory LAUR (2000)
Lavenier, D.: FPGA implementation of the K-means clustering algorithm for hyper spectral images. In: Los Alamos National Laboratory, LAUR # 00-3079, pp. 1-18 (2000)
Gokhale, M., Frigo, J., McCabe, K., Theiler, J., Wolinski, C., Lavenier, D.: Experience with a hybrid processor: K-means clustering. J. Supercomput. 26, 131–148 (2003)
Hussain, H.M., Benkrid, K., Seker, H., Erdogan, A.T.: FPGA implementation of K-means algorithm for bioinformatics application: an accelerated approach to clustering microarray data. In: 2011 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), pp. 248-255 (2011)
Estlick, M., Leeser, M., Theiler, J., Szymanski, J.J.: Algorithmic transformations in the implementation of K-means clustering on reconfigurable hardware. In: Proceedings of the 2001 ACM/SIGDA Ninth International Symposium on Field Programmable Gate Arrays, pp. 103–110 (2001)
Theiler, J., Leeser, M.E., Estlick, M., Szymanski, J.J.: Design issues for hardware implementation of an algorithm for segmenting hyper spectral imagery. In: Descour, M.R., Shen, S.S. (eds.) Imaging Spectrometry VI, vol. 4132, pp. 99–106. The International Society for Optical Engineering, Bellingham, WA (2000)
Bhaskaran, V.: Parameterized Implementation of K-means Clustering on Reconfigurable Systems Citeseer (2004)
Lin, Z., Lo, C., Chow, P.: K-means implementation on FPGA for high-dimensional data using triangle inequality. In: 22nd International Conference on Field Programmable Logic and Applications (FPL) 2012, pp. 437–442 (2012)
Daofu Liu, T.C., Liu, S., Zhou, J., Zhou, S., Temam, O., Feng, X., Zhou, X., Chen, Y.: Pudiannao: a polyvalent machine learning accelerator. In: Proceedings of the 20th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2015) (2015)
Ichikawa, K., Morishita, S.: A simple but powerful heuristic method for accelerating k-means clustering of large-scale data in life science. IEEE/ACM Trans. Comput. Biol. Bioinf. 11(4), 681–692 (2014)
Childs, L., Childs, L.N.: A Concrete Introduction to Higher Algebra, vol. 1. Springer, New York (1979)
Acknowledgments
This work was supported by the National Science Foundation of China under grants (No. 61379040, No. 61272131, No. 61202053), Jiangsu Provincial Natural Science Foundation (No. SBK201240198), Open Project of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences (No. CARCH201407), and the Strategic Priority Research Program of CAS (No. XDA06010403).
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Jia, F., Wang, C., Li, X., Zhou, X. (2015). SAKMA: Specialized FPGA-Based Accelerator Architecture for Data-Intensive K-Means Algorithms. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9529. Springer, Cham. https://doi.org/10.1007/978-3-319-27122-4_8
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DOI: https://doi.org/10.1007/978-3-319-27122-4_8
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