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Title: On microelectronic self-learning cognitive chip systems
Author: Krundel, Ludovic
ISNI:       0000 0004 5919 1761
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 2016
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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 approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche.
Supervisor: Not available Sponsor: EPSRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: 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