SAKMA: Specialized FPGA-Based Accelerator Architecture for Data-Intensive K-Means Algorithms-Reference-Cited by-同舟云学术

SAKMA: Specialized FPGA-Based Accelerator Architecture for Data-Intensive K-Means Algorithms

Published:2015 Issue: Volume: Page:106-119
ISSN:0302-9743
Container-title:Algorithms and Architectures for Parallel Processing
language:
Short-container-title:

Author:

Jia Fahui,Wang Chao,Li Xi,Zhou Xuehai

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-27122-4_8

Reference16 articles.

1. 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)

2. Wang, C., Li, X., Zhou, X.: SODA: software defined FPGA based accelerators for big data, pp. 884-887 (2015)

3. 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)

4. Macgregor, P.F., Squire, J.A.: Application of microarrays to the analysis of gene expression in cancer. Clin. Chem. 48, 1170–1177 (2002)

5. Akay, M.: Genomics and Proteomics Engineering in Medicine and Biology (IEEE Press Series in Biomedical Engineering). John Wiley & Sons, New York (2007)

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning algorithms for FPGA Implementation in biomedical engineering applications: A review;Heliyon;2024-02

2. A survey on parallel clustering algorithms for Big Data;Artificial Intelligence Review;2020-10-06

3. SuperMIC: Analyzing Large Biological Datasets in Bioinformatics with Maximal Information Coefficient;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2017-07-01