| 研究課題/領域番号 |
19K21083
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| 補助金の研究課題番号 |
18H05911 (2018)
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| 研究種目 |
研究活動スタート支援
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| 配分区分 | 基金 (2019)
補助金 (2018) |
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| 審査区分 |
0302:電気電子工学およびその関連分野
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| 研究機関 | 国立研究開発法人産業技術総合研究所 |
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研究代表者 |
Stoliar Pablo 国立研究開発法人産業技術総合研究所, エレクトロニクス・製造領域, 主任研究員 (40824545)
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| 研究期間 (年度) |
2018-08-24 – 2021-03-31
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| 研究課題ステータス |
完了 (2020年度)
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| 配分額 *注記 |
2,990千円 (直接経費: 2,300千円、間接経費: 690千円)
2019年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2018年度: 1,560千円 (直接経費: 1,200千円、間接経費: 360千円)
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| キーワード | Artificial neuron / Neuromorphic system / Mott materials / Jeffress model / Neuromorphic systems / Emerging devices / Recurrent neural net. / Homeostasis mechanisms / Ferroelec. tunnel junct. |
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| 研究開始時の研究の概要 |
AI using neuromorphic circuits is expected to be advantageous in size, speed and energy. Still, human events are very slow-paced for standard electronics. That is not a problem for conventional computers, but neuromorphic systems work differently; they cannot go idle or do multitasking. The solutions using standard silicon technology are very bulky or power hungry. Here, neuromorphic building blocks, architectures and learning methods using resistive switching devices are developed. Overall, this project contributes to the general understanding of how to do AI using emerging devices.
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| 研究成果の概要 |
The results of the projects are described below in english. The results of the projects are described below in english. The results of the projects are described below in english.
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| 研究成果の学術的意義や社会的意義 |
AI using neuromorphic circuits is expected to be advantageous in size, speed, and energy. Nevertheless neuromorphic systems operating at human timecales are bulky and ineficient. Overall, this project studies how emerging devices can be more efficient roadmap for AI-human interaction.
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