WEKO3
アイテム
LUTNet-RC: Look-Up Tables Networks for Reservoir Computing on an FPGA
http://hdl.handle.net/10228/0002000603
http://hdl.handle.net/10228/000200060391e38983-a9ce-4b52-9346-f6bc57c75d19
| 名前 / ファイル | ライセンス | アクション |
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10429120.pdf (3 MB)
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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 | 2024-05-09 | |||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||
| 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 | LUTNet-RC: Look-Up Tables Networks for Reservoir Computing on an FPGA | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||
| 著者 |
Yoshioka, Kanta
× Yoshioka, Kanta
× 田中, 悠一朗
WEKO
30537
× 田向, 権
WEKO
6059
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| 抄録 | ||||||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||||||
| 内容記述 | We propose look-up tables networks-based reservoir computing (LUTNet-RC). This work is the first trial of applying LUTNets to RC. LUTNet-RC consists of a LUT-based reservoir layer and a non-LUT-based output layer. LUTNets have disadvantages such as limited sparse connectivity and weights cannot be changed after implementation. However, when applied to a reservoir layer of RC (LUT-based reservoir layer), these disadvantages are eliminated, because this layer works with sparse connectivity and the weights are fixed, so only the advantage of small circuit resources is obtained. For the LUT-based reservoir layer, we propose and model a multi-bit weight reservoir, modifying the conventional binarized reservoir to improve calculation accuracy. In the case of LUTNets, the proposed multi-bit weight reservoir can be implemented without the increase in utilized circuit resources because LUTNets focus only on the input-output relationship on neurons. Additionally, we propose a speed-up method in the output layer with time division calculation, which compares the current network state with previous states and then calculates only status-changed neurons. As a result, we implement a LUTNet-RC with 1500 reservoir neurons on a field-programmable gate array (KR260) running at 100MHz. The utilized circuit resources are dominated by LUTs, which use approximately 26% of the total amount of LUTs. The LUTNet-RC can infer more than 10 6 data per second. We also verify the LUTNet-RC performance using nonlinear auto-regressive moving average 10 (NARMA10) and the performance is comparable to conventional works. We conclude that the LUTNet-RC is one of the highest-performance RC on an FPGA. | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 備考 | ||||||||||||||||||||
| 内容記述タイプ | Other | |||||||||||||||||||
| 内容記述 | 2023 International Conference on Field Programmable Technology (ICFPT), 12-14 December 2023, Yokohama, Japan | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 書誌情報 |
en : 2023 International Conference on Field Programmable Technology (ICFPT) p. 170-178, 発行日 2024-02-01 |
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| 出版社 | ||||||||||||||||||||
| 出版者 | IEEE | |||||||||||||||||||
| DOI | ||||||||||||||||||||
| 関連タイプ | isVersionOf | |||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||||
| 関連識別子 | https://doi.org/10.1109/ICFPT59805.2023.00024 | |||||||||||||||||||
| ISBN | ||||||||||||||||||||
| 識別子タイプ | ISBN | |||||||||||||||||||
| 関連識別子 | 979-8-3503-5912-1 | |||||||||||||||||||
| ISBN | ||||||||||||||||||||
| 識別子タイプ | ISBN | |||||||||||||||||||
| 関連識別子 | 979-8-3503-5911-4 | |||||||||||||||||||
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| 収録物識別子タイプ | PISSN | |||||||||||||||||||
| 収録物識別子 | 2837-0430 | |||||||||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||||||||
| 収録物識別子 | 2837-0449 | |||||||||||||||||||
| 著作権関連情報 | ||||||||||||||||||||
| 権利情報 | Copyright (c) 2024 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 | UDC | |||||||||||||||||||
| 主題 | neural networks | |||||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||||
| 主題 | reservoir computing | |||||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||||
| 主題 | field programmable gate array | |||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||
| 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 | |||||||||||||||||||
| 研究者情報 | ||||||||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100001426_ja.html | |||||||||||||||||||
| 論文ID（連携） | ||||||||||||||||||||
| 値 | 10429120 | |||||||||||||||||||
| 連携ID | ||||||||||||||||||||
| 値 | 12178 | |||||||||||||||||||
