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Benchmarking Hardware Accelerating Techniques for Extreme Learning Machine

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Proceedings of ELM 2018 (ELM 2018)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 11))

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Abstract

As a popular classification algorithm for machine learning, Extreme Learning Machine (ELM) has been widely used. However, its performance on various hardware devices is unclear. According to the baseline implementation of single core ELM, we find that the main time cost of ELM is matrix multiplication. Then, this paper designs various optimized hardware algorithms for several computing devices (Multi-Core, GPU, and FPGA). According to the experiment of each platform, we can see that the speedup ratio of the new hardware platform to ELM is 4~100+, we open our source code and strongly recommend that the later researchers design the application of ELM algorithm based on appropriate hardware platform.

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Notes

  1. 1.

    http://archive.ics.uci.edu/ml/datasets/Covertype.

  2. 2.

    https://github.com/bombehub/ELM_exp.

  3. 3.

    https://developer.nvidia.com/cublas.

  4. 4.

    http://www.culatools.com/.

  5. 5.

    https://github.com/bombehub/ELM_GPU/.

References

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Acknowledgments

Guoren Wang is supported by the NSFC (Grant No. U1401256, 61732003, 61332006 and 61729201). Gang Wu is supported by the NSFC (Grant No. 61872072) and the State Key Laboratory of Computer Software New Technology Open Project Fund (Grant No. KFKT2018B05).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Liang Li & Gang Wu

  2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Guoren Wang & Qi Zhang

  3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Gang Wu

Authors
  1. Liang Li
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  2. Guoren Wang
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  3. Gang Wu
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  4. Qi Zhang
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Corresponding author

Correspondence to Guoren Wang .

Editor information

Editors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Xiasha, Hangzhou, China

    Jiuwen Cao

  2. Department of Computer and Information Science, University of Macau, Taipa, Macao

    Chi Man Vong

  3. Nokia Bell Labs, Cybersecurity Research, Espoo, Finland

    Yoan Miche

  4. Department of Information and Logistics Technology, University of Houston, Houston, TX, USA

    Amaury Lendasse

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Li, L., Wang, G., Wu, G., Zhang, Q. (2020). Benchmarking Hardware Accelerating Techniques for Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_16

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