dc.citation.conferencePlace |
US |
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dc.citation.title |
IEEE International Conference on Computer Design |
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dc.contributor.author |
Yu, Joonsang |
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dc.contributor.author |
Kim, Jyounghoon |
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dc.contributor.author |
Lee, Jongeun |
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dc.contributor.author |
Choi, Kiyoung |
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dc.date.accessioned |
2023-12-19T18:06:25Z |
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dc.date.available |
2023-12-19T18:06:25Z |
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dc.date.created |
2018-01-06 |
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dc.date.issued |
2017-11-05 |
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dc.description.abstract |
This paper presents an efficient unipolar stochastic computing hardware for convolutional neural networks (CNNs). It includes stochastic ReLU and optimized max function, which are key components in a CNN. To avoid the range limitation problem of stochastic numbers and increase the signal-to-noise ratio, we perform weight normalization and upscaling. In addition, to reduce the overhead of binary-to-stochastic conversion, we propose a scheme for sharing stochastic number generators among the neurons in a CNN. Experimental results show that our approach outperforms the previous ones based on stochastic computing in terms of accuracy, area, and energy consumption. |
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dc.identifier.bibliographicCitation |
IEEE International Conference on Computer Design |
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dc.identifier.doi |
10.1109/ICCD.2017.24 |
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dc.identifier.scopusid |
2-s2.0-85041673085 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/32742 |
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dc.identifier.url |
http://ieeexplore.ieee.org/abstract/document/8119197/ |
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dc.language |
영어 |
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dc.publisher |
IEEE |
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dc.title |
Accurate and Efficient Stochastic Computing Hardware for Convolutional Neural Networks |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2017-11-05 |
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