Abstract: In the past 6 years, we have developed the GeNN (GPU enhanced neuronal networks) framework for GPU accelerated spiking neuronal network simulations. In essence, GeNN is based on a simple design of code generation that allows a large extent of flexibility for computational models while at the same time taking care of some of the GPU specific optimisation work in the background. In this talk I will present the main features of GeNN, its design principles and show benchmarks. I will then discuss limitations, both specific to GeNN and to numerical simulation work on GPUs more generally and present some further work, including the SpineML-GeNN and Brian2GeNN interfaces. GeNN is developed within the Green Brain http://greenbrain.group.shef.ac.uk/ and Brains on Board http://brainsonboard.co.uk/ projects and is available under GPL v2 at Github http://genn-team.github.io/genn/.
Bio: Thomas Nowotny received his PhD in theoretical physics in 2001 from the University of Leipzig and worked for five years at the University of California, San Diego. In 2007 he joined the University of Sussex, where he is now a Professor of Informatics and the Director for Research and Knowledge Exchange at the School of Engineering and Informatics. His research interests include olfaction in animals and machines, GPU accelerated scientific computing, hybrid brain-computer systems and bio-inspired machine learning.
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