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FS Focused Session "AI Electronics" » FS.1 Focused Session "AI Electronics"

[18a-A410-1~9] FS.1 Focused Session "AI Electronics"

Sat. Mar 18, 2023 9:00 AM - 11:30 AM A410 (Building No. 6)

Ryosho Nakane(Univ. of Tokyo), Norio Sato(NTT)

△：Presentation by Applicant for JSAP Young Scientists Presentation Award
▲：English Presentation
▼：Both of Above
No Mark：None of Above

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9:00 AM - 9:15 AM

[18a-A410-1] 【Highlighted Presentation】Reservoir computing utilizing spin waves: enhancement of computational performance through a practical approach for on-chip devices

〇Ryosho Nakane¹, Akira Hirose¹, Gouhei Tanaka^1,2,3 (1.Eng., Univ. of Tokyo, 2.IRCN, Univ. of Tokyo, 3.IST, Univ. of Tokyo)

9:15 AM - 9:30 AM

△ [18a-A410-2] Parallelization and thermal robustness of physical reservoir computing in magnetic materials

〇Kaito Kobayashi¹, Yukitoshi Motome¹ (1.Univ. of Tokyo)

9:30 AM - 9:45 AM

△ [18a-A410-3] Reservoir computing properties of cultured neuronal networks with modular structure

〇Takuma Sumi^1,2, Hideaki Yamamoto², Koki Ito^2,3, Yuichi Katori⁴, Shigeo Sato², Ayumi Hirano-Iwata^1,2,5 (1.Graduate School of Biomedical Engineering, Tohoku Univ., 2.RIEC, Tohoku Univ., 3.School of Engineering, Tohoku Univ., 4.Future Univ. Hakodate, 5.AIMR, Tohoku Univ.)

9:45 AM - 10:00 AM

△ [18a-A410-4] Dependence of operating power and learning accuracy on metal ion concentration in ionic liquid reservoir devices

〇Yuki Kubo^1,2, Takuma Matsuo^1,2, Masaharu Yonezawa^1,2, Hisashi Shima², Yasuhisa Naitoh², Hiroyuki Akinaga², Toshiyuki Itoh³, Toshiki Nokami⁴, Masakazu Kobayashi^1,5, Kentaro Kinoshita¹ (1.Tokyo Univ. of Sci., 2.AIST, 3.Toyota Physical and Chemical Research Institute, 4.Tottori Univ., 5.NAGASE & CO., LTD.)

10:00 AM - 10:15 AM

[18a-A410-5] Effects of anion species on the learning performance of ionic liquid reservoirs

〇Asahi Arai¹, Taiki Koide¹, Hiroyuki Kai¹, Kentaro Kinoshita¹ (1.Tokyo Univ of Sci.)

10:30 AM - 10:45 AM

△ [18a-A410-6] Ion-Gating reservoir using Li⁺ insertion/desertion in (104)oriented LiCoO₂ film

〇Kaoru Shibata^1,2, Daiki Nishioka^1,2, Tomoki Wada^1,2, Wataru Namiki¹, Takashi Tsuchiya^1,2, Tohru Higuchi², Kazuya Terabe¹ (1.NIMS, 2.TUS)

10:45 AM - 11:00 AM

△ [18a-A410-7] Reservoir inspired in-sensor computing device made of CNT-PDMS nanocomposites

〇(M2)Kouki Kimizuka¹, Saman Azhari^1,2, Ikemoto Shuhei^1,2, Usami Yuki^1,2, Tanaka Hirofumi^1,2 (1.Kyushu Inst. Tech. LSSE, 2.Kyushu Inst. Tech. Neumorph Center)

11:00 AM - 11:15 AM

△ [18a-A410-8] A Discrimination of Slipping Condition in Paper Rolling up Based on Tactile Information by Reservoir Computing

〇(M1)Sota Tsubokura¹, Mitsuhito Ando¹, Masanori Kitano¹, Haruo Noma¹ (1.Ritsumeikan Univ.)

11:15 AM - 11:30 AM

△ [18a-A410-9] Computing with a pneumatic artificial muscle: implementations of the length estimation and bifurcation embedding

〇Nozomi Akashi¹, Yasuo Kuniyoshi², Taketomo Jo³, Mitsuhiro Nishida³, Ryo Sakurai³, Yasumichi Wakao³, Kohei Nakajima² (1.Kyoto Univ., 2.Univ. Tokyo, 3.Bridgestone)

Session information

[18a-A410-1~9] FS.1 Focused Session "AI Electronics"

[18a-A410-1] 【Highlighted Presentation】Reservoir computing utilizing spin waves: enhancement of computational performance through a practical approach for on-chip devices

△ [18a-A410-2] Parallelization and thermal robustness of physical reservoir computing in magnetic materials

△ [18a-A410-3] Reservoir computing properties of cultured neuronal networks with modular structure

△ [18a-A410-4] Dependence of operating power and learning accuracy on metal ion concentration in ionic liquid reservoir devices

[18a-A410-5] Effects of anion species on the learning performance of ionic liquid reservoirs

△ [18a-A410-6] Ion-Gating reservoir using Li⁺ insertion/desertion in (104)oriented LiCoO₂ film

△ [18a-A410-7] Reservoir inspired in-sensor computing device made of CNT-PDMS nanocomposites

△ [18a-A410-8] A Discrimination of Slipping Condition in Paper Rolling up Based on Tactile Information by Reservoir Computing

△ [18a-A410-9] Computing with a pneumatic artificial muscle: implementations of the length estimation and bifurcation embedding

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Session information

[18a-A410-1~9] FS.1 Focused Session "AI Electronics"

[18a-A410-1] 【Highlighted Presentation】Reservoir computing utilizing spin waves: enhancement of computational performance through a practical approach for on-chip devices

△ [18a-A410-2] Parallelization and thermal robustness of physical reservoir computing in magnetic materials

△ [18a-A410-3] Reservoir computing properties of cultured neuronal networks with modular structure

△ [18a-A410-4] Dependence of operating power and learning accuracy on metal ion concentration in ionic liquid reservoir devices

[18a-A410-5] Effects of anion species on the learning performance of ionic liquid reservoirs

△ [18a-A410-6] Ion-Gating reservoir using Li+ insertion/desertion in (104)oriented LiCoO2 film

△ [18a-A410-7] Reservoir inspired in-sensor computing device made of CNT-PDMS nanocomposites

△ [18a-A410-8] A Discrimination of Slipping Condition in Paper Rolling up Based on Tactile Information by Reservoir Computing

△ [18a-A410-9] Computing with a pneumatic artificial muscle: implementations of the length estimation and bifurcation embedding

Authenticate

△ [18a-A410-6] Ion-Gating reservoir using Li⁺ insertion/desertion in (104)oriented LiCoO₂ film