WEKO3
アイテム
An Implementation Method Using Cut-Off Bits for Restricted Boltzmann Machines Without Random Number Generators
http://hdl.handle.net/10228/0002001330
http://hdl.handle.net/10228/00020013305c6c90fc-d505-4357-a684-5ff5db907a0b
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
10444481.pdf (890 KB)
|
|
| Item type | 共通アイテムタイプ(1) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2025-02-17 | |||||||||||
| タイトル | ||||||||||||
| タイトル | An Implementation Method Using Cut-Off Bits for Restricted Boltzmann Machines Without Random Number Generators | |||||||||||
| 言語 | en | |||||||||||
| 著者 |
Hori, Sansei
× Hori, Sansei
× 田向, 権
WEKO
6059
|
|||||||||||
| 著作権関連情報 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||||
| 権利情報 | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | |||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | This study proposes an implementation method of a hardware-oriented restricted Boltzmann machine (RBM) without random number generators (RNGs) that employ cut-off bits, which are obtained from fixed-point binary arithmetic operations on digital hardware, such as field-programmable gate arrays (FPGAs), instead of random numbers. Most FPGA circuits employ fixed-point binary arithmetic operations to improve hardware resource efficiency. Therefore, the proposed method applies the unique feature of the operation, which is bit width extension and cut-off bits. Stochastic neural networks, including RBMs, employ sampling processes based on a probability distribution associated with the network, and the processes require many random numbers. However, implementing RNGs in hardware is costly because it requires considerable hardware resources. The proposed method can mitigate this requirement. To validate the proposed method, we implement an RBM with the proposed method on the software, emulate fixed-point binary arithmetic operations, and train the RBM using the MNIST and Fashion MNIST datasets. Furthermore, we apply the chi-square goodness-of-fit test to evaluate the uniformity of the cut-off bits. Additionally, we compare hardware resource requirements and power consumption for the proposed method and some major RNGs, a linear feedback shift register (LFSR), and a xorshift. Experimental results showed that it was possible to use the cut-off bits for training the RBM using the datasets and clarified the properties of the cut-off bits using statistical analyses. Moreover, hardware implementation of the proposed method involved the lowest hardware resource requirements and power consumption among the RNGs compared in this study. | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
en : IEEE Access 巻 10, p. 42791-42801, 発行日 2022-04-18 |
|||||||||||
| 出版社 | ||||||||||||
| 出版者 | IEEE | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Field-programmable gate arrays | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | neural networks | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | random number generation | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | restricted Boltzmann machines | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 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 | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| DOI | ||||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1109/ACCESS.2022.3168026 | |||||||||||
| 助成情報 | ||||||||||||
| 助成機関名 | UENO SEIKI Next Generation Frontier Technology Collaboration Laboratory | |||||||||||
| 言語 | en | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2169-3536 | |||||||||||
| 研究者情報 | ||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100000641_ja.html | |||||||||||
| 論文ID（連携） | ||||||||||||
| 値 | 10444481 | |||||||||||
| 連携ID | ||||||||||||
| 値 | 12628 | |||||||||||
