| アイテムタイプ |
共通アイテムタイプ(1) |
| 公開日 |
2025-02-13 |
| タイトル |
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|
タイトル |
CMOS digital-analog mixed signal VLSI implementation of a hippocampus-inspired model |
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言語 |
en |
| その他のタイトル |
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その他のタイトル |
CMOS Digital-Analog Mixed Signal VLSI Implementation of a Hippocampus-Inspired Model |
|
言語 |
en |
| 著者 |
Shishido, Yuka
野村, 修
立野, 勝巳
田向, 権
森江, 隆
|
| 著作権関連情報 |
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|
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. |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
For artificial intelligence (AI) to be useful in the home, it is required to acquire unique knowledge of the home obtained through interaction with space and environment. This is difficult for deep learning-based AI. The human brain can learn unique knowledge from few experiences. The entorhinal cortex and hippocampus play essential roles for episodic memory formation and recall. While entorhinal-hippocampal models have been proposed that can reproduce episodic memory, hardware systems that implement such models face challenges related to high computational complexity, power consumption, and processing speed. In this paper, we propose a digital-analog mixed-signal CMOS VLSI implementation of a hippocampus-inspired model that can memorize and associate place and object information essential for the formation of episodic memory. By using both analog and digital in-memory computing architecture, the proposed circuit has achieved a computational efficiency of 22 TOPS/W, which is very high for AI hardware with a learning function. The proposed circuit was fabricated, measured, and evaluated. The results of an experiment using a fabricated chip and a control system showed that the proposed circuit can memorize and process place and object information, and can acquire environment-unique knowledge through interaction with a space. |
|
言語 |
en |
| 備考 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
2024 International Joint Conference on Neural Networks, IJCNN 2024, 30 June - 05 July 2024, Yokohama, Japan |
|
言語 |
en |
| 書誌情報 |
en : 2024 International Joint Conference on Neural Networks (IJCNN)
発行日 2024-09-09
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| 出版社 |
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出版者 |
IEEE |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Episodic memory |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Hippocampus |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
In-memory computing |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Neuromorphic hardware |
| 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 |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1109/IJCNN60899.2024.10650772 |
| ISBN |
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|
識別子タイプ |
ISBN |
|
|
関連識別子 |
979-8-3503-5931-2 |
| ISSN |
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|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2161-4407 |
| 会議記述 |
|
|
|
会議名 |
2024 International Joint Conference on Neural Networks, IJCNN 2024 |
|
|
言語 |
en |
|
|
開始年 |
2024 |
|
|
開始月 |
06 |
|
|
開始日 |
30 |
|
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終了年 |
2024 |
|
|
終了月 |
07 |
|
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終了日 |
05 |
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開催国 |
JPN |
| 研究者情報 |
|
|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/339_ja.html |
| 研究者情報 |
|
|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/355_ja.html |
| 研究者情報 |
|
|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/100000641_ja.html |
| 論文ID（連携） |
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|
値 |
10444651 |
| 連携ID |
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|
値 |
12590 |
10444651.pdf (10.1 MB)
