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
Link To Document