Exploring Liquid Computing in a Hardware Adaptation : Construction and Operation of a Neural Network Experiment

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

The SAND Neurochip and its Embedding in the MiND System

The system MiND (Multipurpose integrated Neural Device) is a tool for the development of artificial neural network applications which integrates hardware and software components. It includes a PCI neuro-board with up to four SAND (Simple Applicable Neural Device) neuro-chips. The neuro-board accelerates feedforward networks, Radial- Basis-Function networks, and Kohonen feature maps. There are several simple to use software layers for exploiting the neuro--board. At the bottom, there is the driver's C interface. Secondly, a number of C++ network classes are built on the C-drivers. Thirdly, comfortable simulators with graphical interfaces base on the C++ classes. These stem from a pool of "predefined" simulators provided by the MiND system. Each simulator is constituted by a network definition written in the neural network description language CONNECT, and by an interface definition script. The interface definition is based on a C++ network class generated from the CONNECT definit..

The SAND Neurochip and its Embedding in the MiND System

. The system MiND (Multipurpose integrated Neural Device) is a tool for the development of artificial neural network applications which integrates hardware and software components. It includes a PCI neuro--board with up to four SAND (Simple Applicable Neural Device) neuro--chips. The neuro--board accelerates feedforward networks, Radial-- Basis-Function networks, and Kohonen feature maps. There are several simple to use software layers for exploiting the neuro--board. At the bottom, there is the driver's C interface. Secondly, a number of C++ network classes are built on the C--drivers. Thirdly, comfortable simulators with graphical interfaces base on the C++ classes. These stem from a pool of "predefined" simulators provided by the MiND system. Each simulator is constituted by a network definition written in the neural network description language CONNECT, and by an interface definition script. The interface definition is based on a C++ network class generated from the CONNECT definit..