@article{oai:kyutech.repo.nii.ac.jp:02000027,
 author = {Tanaka, Yuichiro and 田中, 悠一朗 and Usami, Yuki and 宇佐美, 雄生 and Tanaka, Hirofumi and 田中, 啓文 and Tamukoh, Hakaru and 田向, 権},
 journal = {2023 IEEE International Symposium on Circuits and Systems (ISCAS)},
 month = {Jul},
 note = {This study aims to implement a reservoir-based convolutional neural network (CNN) on physical reservoir computing (RC) to develop an efficient image recognition system for edge AI. Therefore, we propose a novel reservoir-based convolution circuit system that uses in-material reservoir computing, a type of physical RC made from a sulfonated polyaniline network. The experimental results demonstrate that the proposed circuit system extracts image features in the same way as the original CNN and that a reservoir-based CNN on the in-material RC achieves an accuracy rate of 81.7% in an image classification task while an echo state network-based CNN achieves 87.7%., 2023 IEEE International Symposium on Circuits and Systems, ISCAS 2023, 21-25 May, 2023, Monterey, California, USA},
 title = {In-material reservoir implementation of reservoir-based convolution},
 year = {2023},
 yomi = {タナカ, ユウイチロウ and ウサミ, ユウキ and タナカ, ヒロフミ and タムコウ, ハカル}
}