Performance of Ag-Ag2S core-shell nanoparticle-based random network reservoir computing device

Hadiyawarman, Yuki Usami*, Takumi Kotooka, Saman Azhari, Masanori Eguchi, Hirofumi Tanaka

*この研究の対応する著者

研究成果: Article査読

2 被引用数 (Scopus)

抄録

Reservoir computing (RC), a low-power computational framework derived from recurrent neural networks, is suitable for temporal/sequential data processing. Here, we report the development of RC devices utilizing Ag-Ag2S core-shell nanoparticles (NPs), synthesized by a simple wet chemical protocol, as the reservoir layer. We examined the NP-based reservoir layer for the required properties of RC hardware, such as echo state property, and then performed the benchmark tasks. Our study on NP-based reservoirs highlighted the importance of the dynamics between the NPs as indicated by the rich high dimensionality due to the echo state property. These dynamics affected the accuracy (up to 99%) of the target waveforms that were generated with a low number of readout channels. Our study demonstrates the great potential of Ag-Ag2S NPs for the development of next-generation RC hardware.

本文言語English
論文番号SCCF02
ジャーナルJapanese journal of applied physics
60
SC
DOI
出版ステータスPublished - 2021 6月
外部発表はい

ASJC Scopus subject areas

  • 工学(全般)
  • 物理学および天文学(全般)

フィンガープリント

「Performance of Ag-Ag2S core-shell nanoparticle-based random network reservoir computing device」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル