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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/21804

Title: On microelectronic self-learning cognitive chip systems
Authors: Krundel, Ludovic
Keywords: Machine learning
Cellular automata
Neural networks
Field-Programmable Gate Array (FPGA) devices
Hardware description languages
Asynchronous design
Simultaneous parallel processes
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
Issue Date: 2016
Publisher: © Ludovic Alain Krundel
Abstract: 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.
Description: A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.
Sponsor: Holywell Park from EPSRC and myself
URI: https://dspace.lboro.ac.uk/2134/21804
Appears in Collections:PhD Theses (Mechanical, Electrical and Manufacturing Engineering)

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