TY  - GEN
Y1  - 2005///
KW  - Liquid Computing 
KW  -  edge of chaos 
KW  -  real-time computingLiquid Computing 
KW  -  edge of chaos 
KW  -  real-time computing
TI  - Exploring Liquid Computing in a Hardware Adaptation : Construction and Operation of a Neural Network Experiment
ID  - heidok5615
N2  - Future increases in computing power strongly rely on miniaturization, large scale integration, and parallelization. Yet, approaching the nanometer realm poses new challenges in terms of device reliability, power dissipation, and connectivity - issues that have been of lesser concern in today's prevailing microprocessor implementations. It is therefore necessary to pursue the research on alternative computing architectures and strategies that can make use of large numbers of unreliable devices and only have a moderate power consumption. This thesis describes the construction of an experiment dedicated to exploring silicon adaptations of artificial neural network paradigms for their general applicability, power efficiency, and fault-tolerance. The presented setup comprises peripheral electronics, programmable logic, and software to accommodate a mixed-signal CMOS microchip implementing a flexible perceptron with 256 McCulloch-Pitts neurons. This neural network experiment is used to explore a recent strategy that allows to access the power of recurrent network topologies. While it has been conjectured that this liquid computing is suited for hardware implementations, this first time adaptation to a CMOS neural network affirms this claim. Not only feasibility but also tolerance to substrate variations and robustness to faults during operation are demonstrated.
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/5615/
AV  - public
A1  - Schürmann, Felix
ER  -