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Applications of Emerging Memory Technology pp 177–190Cite as

Greedy Edge-Wise Training of Resistive Switch Arrays

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Part of the book series: Springer Series in Advanced Microelectronics ((MICROELECTR.,volume 63))

Abstract

A technical challenge of machine learning based on artificial neural network is large-scale multiply-accumulate (MAC) operation that is costly. The larger the network, the more MAC operations are required for inference as well as training. As an alternative to this conventional digital MAC operation, a resistive switching memory array realizes analog MAC operations in a fully parallel manner. Algorithms for training such an array are mainly taken from conventional machine learning algorithms such as backpropagation. They are also customized such that they are suitably implemented on the array. In this chapter, we address such customized machine learning algorithm as well as new algorithms that are barely based on the conventional machine learning. A particular focus will be placed on a recently proposed greedy edge-wise training algorithm that is suitable for resistive switching memory arrays.

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Author information

Authors and Affiliations

  1. Division of Materials Science and Engineering, Hanyang University, Wangsimni-ro 222, Seongdong-ku, 04763, Seoul, Republic of Korea

    Doo Seok Jeong

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  1. Doo Seok Jeong
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Correspondence to Doo Seok Jeong .

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Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Manan Suri

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Jeong, D.S. (2020). Greedy Edge-Wise Training of Resistive Switch Arrays. In: Suri, M. (eds) Applications of Emerging Memory Technology. Springer Series in Advanced Microelectronics, vol 63. Springer, Singapore. https://doi.org/10.1007/978-981-13-8379-3_7

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  • DOI: https://doi.org/10.1007/978-981-13-8379-3_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-8378-6

  • Online ISBN: 978-981-13-8379-3

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