28-m W Fully Embedded AI Techniques with On-site Learning for Low-Power Handy Tactile Sensing System-Reference-Cited by-同舟云学术

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28-m W Fully Embedded AI Techniques with On-site Learning for Low-Power Handy Tactile Sensing System

Published:2022-04-18 Issue: Volume: Page:
ISSN:
Container-title:2022 International Symposium on VLSI Design, Automation and Test (VLSI-DAT)
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Author:

Yano Yuji¹,Iwamoto Hisashi¹,Yoshimura Takuma¹,Nishida Yoshihiro²,Mori Tatsuya³,Komoku Kiyotaka⁴,Takao Hidekuni²,Arimoto Kazutami⁴

Affiliation:

1. poco-apoco Networks,Development department,Osaka,Japan

2. Kagawa University,Faculty of Engineering and Design,Takamatsu,Japan

3. FaMT Himeji,Development department,Japan

4. Okayama Prefectural University,Faculty of Computer Science and Systems Engineering,Sojya,Japan

Funder

JST-CREST

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9768045/9768046/09768071.pdf?arnumber=9768071

Reference5 articles.

1. Normally-off computing for IoT systems

2. Post-CMOS Compatible Silicon MEMS Nano- Tactile Sensor for Touch Feeling Discrimination of Materials;takao;The Electrochemical Society,2020

3. Detection of minute defects using transfer learning-based CNN models

4. Robust material classification with a tactile skin using deep learning

5. Tactile object recognition using deep learning and dropout

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