On microelectronic self-learning cognitive chip systems

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approa...

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Bibliographic Details
Main Author: Ludovic Krundel
Format: Default Thesis
Published: 2016
Subjects:
Mechanical engineering not elsewhere classified
Machine learning
Cybernetics
Cellular automata
Neural networks
Field-Programmable Gate Array (FPGA) devices
Hardware description languages
Asynchronous design
Simultaneous parallel processes
Wetware
Morphware chips
Learning algorithms
Growth rules
Reconnection method policies
Cognitive architectures
Microelectronic mental properties
Human-machine interactions
Ethical issues in robotics
Machine intelligence
Artificial capabilities
Mechanical Engineering not elsewhere classified
Online Access:https://hdl.handle.net/2134/21804
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https://hdl.handle.net/2134/21804

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