Research activity

The social cooperation program “Energy-efficient information processing” aims to develop energy-efficient computing systems and devices based on new operation principles by cooperation between system scientists studying theory and algorithms and device scientists responsible for experiments and developments.

Research topics

Due to recent development of brain function-measurement and image-processing techniques, it has been found that neuronal connectivity in the brain of living things is complex but has modularity and sparseness to some extent. Inspired by the structure of the real brain with regularity and complexity, we aim to explore the connection topology of operation units in energy-efficient computing systems with high performance. Considering hardware implementation of the energy-efficient computing systems in the future, we are developing the optimal network structure of operation units and learning algorithms that can maximize the performance of neural information processing.

There are three major issues to be overcome for implementing efficient information processing systems with parallel distributed processing using computing machinery consisting of solid operational devices: the realization of a huge number of interconnections between operational unit devices; the development of operational devices with a huge number of fan-in and fan-out; the development of individual operational unit devices with extremely low power consumption. These issues are difficult to be handled by the conventional wiring and device technology for CMOS integrated circuits. Meanwhile, many device researchers have tried to implement neuronal computers on chips in a manner different from the CMOS integrated circuits, although large-scale neuronal computers have yet to be implemented by overcoming the above difficulties. One of the reasons of this is that most studies have focused on the specialty of and the interest on the input-output characteristics of devices but have not considered the methodology for network construction of the operational unit devices. This social cooperation program is dealing with the development of operational devices which are required for realization of large-scale integrated neural computers through a collaboration among researchers studying networks/algorithms and those studying devices.

Publications

Journal papers

Conference papers

Invited talks

  • 田中 剛平
    リザバーコンピューティングの数理とデバイス実装
    応用物理学会春季学術講演会(上智大学, 2020年3月12-15日)、JST CREST「光ニューラルネットワークの時空間ダイナミクスに基づく計算基盤技術」シンポジウム、招待講演,3月12日 (2020), 予定.
  • 田中 剛平
    リザバーコンピューティングの基礎と最近の発展
    学振151委員会「先端ナノデバイス・材料テクノロジー」(理化学研究所),招待講演,1月23日 (2020),予定.
  • A. Hirose, R. Nakane, G. Tanaka
    Physical Reservoir Computing Devices: Truly Neural Hardware in the AI and Sensor-Network Era
    International Conference on Neural Information Processing (Sydney, Dec. 12-15), Invited talk (2019).
  • 廣瀬 明,武田 征士,ヘロー・ジャンベノ,沼田 英俊,金澤 直輝,山根 敏志,中野 大樹,中根 了昌,田中 剛平
    光波による物理リザバーコンピューティング
    Optics & Photonics Japan 2019 (Osaka, 2019年12月2-5日), 招待講演, 12月4日 (2019).
  • 田中 剛平
    物理リザバーコンピューティングの最新動向
    人工知能学会合同研究会 ナチュラルコンピューティング研究会(慶應大学),招待講演,11月22日 (2019).
  • A. Hirose, R. Nakane, G. Tanaka
    Physical Reservoir Computing Devices and Complex-Valued Neural Networks
    The 2019 International Meeting for Future of Electron Devices, Kansai (Kyoto, Nov. 14-15), Invited talk, Nov.14 (2019).
  • 中根 了昌
    チップ実装を指向したリザバーコンピューティング研究の現状
    CREST・さきがけ「素材・デバイス・システム融合による革新的ナノエレクトロニクスの創成」領域主催,リザバーコンピューティングワークショップ~基礎から最前線まで~(JST東京本部),依頼講演,10月24日 (2019).
  • 田中 剛平
    リザバーコンピューティング:高速学習による時系列情報処理
    非ノイマン型情報処理へ向けたデバイス技術分科会(大手町),一般社団法人 電子情報技術産業協会(JEITA), 招待講演, 10月16日 (2019),予定.
  • R. Nakane, G. Tanaka, A. Hirose
    Machine-learning computation utilizing spin waves,
    第43回 日本磁気学会学術講演会 (2019年9月25-27日),口頭発表,9月27日, 招待講演, 発表番号27pB-1 (2019).
  • 田中 剛平
    リザバーコンピューティングの数理とハードウェア
    大阪大学非線形数理セミナー, 招待講演, 6月26日 (2019).
  • Gouhei Tanaka
    Recent advances in physical reservoir computing
    The 3rd Neuromorphic Research Retreat in AIST (National Institute of Advanced Industrial Science and Technology (AIST), Oct. 31), Invited talk (2018).
  • Gouhei Tanaka
    Mathematical approach to energy efficient neural information processing
    The 2nd Neuromorphic Research Retreat in AIST (National Institute of Advanced Industrial Science and Technology (AIST), Feb. 17), Invited talk (2018).
  • 田中 剛平
    省エネルギー脳型情報処理への数理的アプローチ
    平成29年度 第4回ブレインウェア研究会(東北大学電気通信研究所, 2月7日), 招待講演 (2018).

Books

Reviews

Awards