WEKO3
アイテム
Efficient Repetition Coding for Deep Learning Towards Implementation Using Emerging Non-Volatile Memory with Write-Errors
http://hdl.handle.net/10228/0002000800
http://hdl.handle.net/10228/0002000800f2eae2b3-46f3-4ec8-a7c8-884c36ca95c2
| 名前 / ファイル | ライセンス | アクション |
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neuro_72.pdf (335 KB)
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| アイテムタイプ | 学術雑誌論文 = Journal Article(1) | |||||||||||||||||||
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| 公開日 | 2024-06-18 | |||||||||||||||||||
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| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||
| タイトル | ||||||||||||||||||||
| タイトル | Efficient Repetition Coding for Deep Learning Towards Implementation Using Emerging Non-Volatile Memory with Write-Errors | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||
| 著者 |
Fuengfusin, Ninnart
× Fuengfusin, Ninnart
× 田向, 権
WEKO
6059
× 田中, 悠一朗
WEKO
30537
× 野村, 修× 森江, 隆 |
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| 抄録 | ||||||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||||||
| 内容記述 | Emerging non-volatile memory devices, such as resistive random access memory (ReRAM) and voltage-controlled magnetoresistive random access memory (VC-MRAM), promise low energy consumption for artificial intelligence applications. However, when implementing deep neural networks (DNNs) using such memory devices, write-error may cause millions of bit-flipping to DNN. This easily degrades the DNN performance. To address this problem, we propose a novel repetition coding for deep-learning (RC-DL), which is a repetition coding designed to protect IEEE 32-bit floating-point (FP32) DNN models. Compared to conventional repetition coding, the proposed RC-DL exploits FP32 non-uniform magnitude encoding by increasing the repeat rates to protect sensitive bit positions and reduce the repeat rates to insensitive bit positions. Hence, RC-DL uses a number of bits equivalent to a 3-bit repetition code while delivering the performance close to 11-bit repetition code. We perform extensive Monte Carlo simulations to simulate the write-error property with ImageNet 2012 pretrained models. The DNN models with RC-DL are shown to be operable in the extremely imperfect environment while delivering with only minor reductions in DNN performance. | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 書誌情報 |
en : 2023 International Joint Conference on Neural Networks (IJCNN) p. 1-6, 発行日 2023-08-02 |
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| 出版者 | IEEE | |||||||||||||||||||
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| 識別子タイプ | DOI | |||||||||||||||||||
| 関連識別子 | https://doi.org/10.1109/IJCNN54540.2023.10191433 | |||||||||||||||||||
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| 識別子タイプ | ISBN | |||||||||||||||||||
| 関連識別子 | 978-1-6654-8867-9 | |||||||||||||||||||
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| 識別子タイプ | ISBN | |||||||||||||||||||
| 関連識別子 | 978-1-6654-8868-6 | |||||||||||||||||||
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| 収録物識別子タイプ | PISSN | |||||||||||||||||||
| 収録物識別子 | 2161-4393 | |||||||||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||||||||
| 収録物識別子 | 2161-4407 | |||||||||||||||||||
| 著作権関連情報 | ||||||||||||||||||||
| 権利情報 | Copyright (c) 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||
| 主題 | error correction code | |||||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||||
| 主題 | deep learning | |||||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||||
| 主題 | emerging memory | |||||||||||||||||||
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| 出版タイプ | AM | |||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||||||||
| 査読の有無 | ||||||||||||||||||||
| 値 | yes | |||||||||||||||||||
