Benchmarking Hardware Accelerating Techniques for Extreme Learning Machine
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-23307-5_16
Reference6 articles.
1. Yeam, T.C., Ismail, N., Mashiko, K., Matsuzaki, T.: FPGA implementation of extreme learning machine system for classification. In: 2017 IEEE Region 10 Conference, TENCON 2017, pp. 1868–1873. IEEE (2017)
2. Frances-Villora, J.V., Rosado-Muñoz, A., Martínez-Villena, J.M., Bataller-Mompean, M., Guerrero, J.F., Wegrzyn, M.: Hardware implementation of real-time extreme learning machine in FPGA: analysis of precision, resource occupation and performance. Comput. Electr. Eng. 51, 139–156 (2016)
3. Safaei, A., Wu, Q.J., Yang, Y., Akılan, T.: System-on-a-Chip (SoC)-based hardware acceleration for extreme learning machine. In: 2017 24th IEEE International Conference on Electronics, Circuits and Systems (ICECS), pp. 470–473. IEEE (2017)
4. Van Heeswijk, M., Miche, Y., Oja, E., Lendasse, A.: GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing 74(16), 2430–2437 (2011)
5. Alia-Martinez, M., Antoñanzas, J., Antonanzas-Torres, F., Pernía-Espinoza, A., Urraca, R.: A straightforward implementation of a GPU-accelerated ELM in R with NVIDIA graphic cards. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 656–667. Springer (2015)
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