• DocumentCode
    3292138
  • Title

    Autonomous learning design in system-on-chip

  • Author

    Yimin Zhou ; Krundel, Ludovic ; Mulvaney, David ; Chouliaras, Vassilios ; Guoqing Xu ; Guoqiang Fu

  • Author_Institution
    Shenzhen Inst. of Adv. Technol., Shenzhen, China
  • fYear
    2013
  • fDate
    12-14 Dec. 2013
  • Firstpage
    1054
  • Lastpage
    1059
  • Abstract
    The solving strategy of artificial intelligence (AI) is adopted with bottom-up design to solve its hard problems. To tackle end-to-end AI-hard problems, a highly self-adaptive control system-on-chip has been developed to self-learn its internal and external resources with the aid of sets of sensors and actuators. Inspired by biological cell learning theory, different approaches of modelling techniques have been derived together with machine learning methods to the embedded control systems so as to perform different tasks. Some experimental results have shown the developments.
  • Keywords
    cellular automata; learning (artificial intelligence); system-on-chip; actuators; artificial intelligence; autonomous learning design; biological cell learning theory; bottom-up design; embedded control systems; end-to-end AI-hard problems; machine learning methods; self-adaptive control system-on-chip; sensors; Artificial neural networks; Biological neural networks; Biological system modeling; Field programmable gate arrays; Hardware; Neurons; Robots;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on
  • Conference_Location
    Shenzhen
  • Type

    conf

  • DOI
    10.1109/ROBIO.2013.6739603
  • Filename
    6739603