12–16 Sept 2005
Heidelberg
Europe/Zurich timezone

Implementing Artificial Neural Networks in Mixed-Mode VLSI

16 Sept 2005, 09:00
45m
Heidelberg

Heidelberg

Germany

Speaker

Prof. Joannes Schemmel (Kirchhoff Institut fuer Physik / Electronic Vision(s))

Description

This talk presents different VLSI models of artificial neural networks ranging from abstract ones using binary neurons to biologically inspired pulse-coupled systems. Circuit examples demonstrating common design principles for optimizing area usage and network speed are shown. The usage of digital communication protocols allows the parallelization of the analog network cores to create large-scale artificial network systems. A key aspect of neural network research is training. Specific training algorithms for simple and complex electronic neuron models have been investigated. For binary models, evolutionary- as well as liquid-computing has proven to be successful. While these methods operate on a global level, the pulse-coupled systems use a local learning rule inspired by contemporary neuroscience called 'spike time dependent plasticity' (STDP). Therefore the VLSI system allows the investigation of important aspects of natural neural plasticity at a speed several orders faster than biological real time.

Author

Prof. Joannes Schemmel (Kirchhoff Institut fuer Physik / Electronic Vision(s))

Presentation materials

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