Thesis-2016-Krundel.pdf (4.41 MB)
On microelectronic self-learning cognitive chip systems
thesisposted on 2016-06-24, 08:16 authored by Ludovic Krundel
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.
Holywell Park from EPSRC and myself
- Mechanical, Electrical and Manufacturing Engineering
Publisher© Ludovic Alain Krundel
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
NotesA Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.
Machine learningCyberneticsCellular automataNeural networksField-Programmable Gate Array (FPGA) devicesHardware description languagesAsynchronous designSimultaneous parallel processesWetwareMorphware chipsLearning algorithmsGrowth rulesReconnection method policiesCognitive architecturesMicroelectronic mental propertiesHuman-machine interactionsEthical issues in roboticsMachine intelligenceArtificial capabilitiesMechanical Engineering not elsewhere classified