Abstract
With growth of data sets, the efficiency of Extreme Learning Machine (ELM) model combined with accustomed hardware implementation such as Field-programmable gate array (FPGA) became attractive for many real-time learning tasks. In order to reduce resource occupation in eventual trained model on FPGA, it is more efficient to store fixed-point data rather than double-floating data in the on-chip RAMs. This paper conducts the fixed-point evaluation of ELM for classification. We converted the ELM algorithm into a fixed-point version by changing the operation type, approximating the complex function and blocking the large-scale matrixes, according to the architecture ELM would be implemented on FPGA. The performance of classification with single bit-width and mixed bit-width were evaluated respectively. Experimental results show that the fixed-point representation used on ELM does work for some application, while the performance could be better if we adopt mixed bit-width.
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This work is funded by National Science Foundation of China(number 61303070).
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Xu, Y., Jiang, J., Jiang, J., Liu, Z., Xu, J. (2016). Fixed-Point Evaluation of Extreme Learning Machine for Classification. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-28397-5_3
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DOI: https://doi.org/10.1007/978-3-319-28397-5_3
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