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Memristive Extreme Learning Machine: A Neuromorphic Implementation

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Proceedings of ELM-2017 (ELM 2017)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 10))

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Abstract

Neuromorphic computation has been a hot research area over the past few years. Memristor, as one of the neuromorphic computation materials memorizes the conductance value and is able to adapt it according to changing voltages. This paper pioneers a neuromorphic computing paradigm implementation (through memristor) for Extreme Learning Machine (ELM), which is one of most popular machine learning methods. By simulating the biological synapses with memristors and combining the memory property of memristor with high-efficient processing ability in ELM, a three-layer ELM model for classification is constructed. We represent the ELM network weights through memristive conductance values. The conductance values (network weights) are updated through tuning the voltages. Experimental results over the Iris dataset show that the memristor-based ELM achieves the same level performance as the one implemented via traditional software, and exhibits great potential that ELM can be implemented in neromorphic computation paradigms.

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Authors and Affiliations

  1. Center for Robotics, School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Lu Zhang, Hong Cheng, Huanghuang Liang, Yang Zhao, Yuansheng Luo & Hongliang Guo

  2. State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Xinqiang Pan & Yao Shuai

Authors
  1. Lu Zhang
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  2. Hong Cheng
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  3. Huanghuang Liang
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  4. Yang Zhao
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  5. Xinqiang Pan
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  6. Yuansheng Luo
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  7. Hongliang Guo
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  8. Yao Shuai
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Corresponding author

Correspondence to Hong Cheng .

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Editors and Affiliations

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

    Jiuwen Cao

  2. Department of Computer and Information Science, University of Macau, Macau, China

    Chi Man Vong

  3. Nokia Bell Labs, Espoo, Finland

    Yoan Miche

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

    Amaury Lendasse

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Zhang, L. et al. (2019). Memristive Extreme Learning Machine: A Neuromorphic Implementation. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM-2017. ELM 2017. Proceedings in Adaptation, Learning and Optimization, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-01520-6_11

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