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Stochastic learning with Back Propagation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Guhyun | - |
dc.contributor.author | Hwang, Cheol Seong | - |
dc.contributor.author | Jeong, Doo Seok | - |
dc.date.accessioned | 2022-10-19T05:22:02Z | - |
dc.date.available | 2022-10-19T05:22:02Z | - |
dc.date.created | 2022-10-17 | - |
dc.date.issued | 2019-05 | - |
dc.identifier.citation | 2019 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), p. 8702253 | - |
dc.identifier.issn | 0271-4302 | - |
dc.identifier.uri | https://hdl.handle.net/10371/186497 | - |
dc.description.abstract | Despite of remarkable progress on deep learning, its hardware implementation beyond deep learning acceleration is still behind the software deep learning due in part to lack of hardware-compatible learning algorithm. In this paper, a learning method called the stochastic learning with backpropagation (SLBP) algorithm was proposed. The network of concern consists of ternary synaptic weight, favorable to be implemented in a resistance-based crossbar array. Every training epoch, the SLBP algorithm evaluates weight update probability at which the corresponding weight is updated in a stochastic manner. The algorithm was used to train a denoising autoencoder, which identified the successful reduction in noise (increase in peak signal-to-noise ratio by approximately 68%). Notably, the SLBP algorithm achieves an 86% reduction in memory usage compared with a real-valued autoencoder trained using a backpropagation algorithm. | - |
dc.language | 영어 | - |
dc.publisher | IEEE | - |
dc.title | Stochastic learning with Back Propagation | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ISCAS.2019.8702253 | - |
dc.citation.journaltitle | 2019 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) | - |
dc.identifier.wosid | 000483076400173 | - |
dc.identifier.scopusid | 2-s2.0-85066803913 | - |
dc.citation.startpage | 8702253 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Hwang, Cheol Seong | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
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