Abstract
The GPU enhanced neuronal networks (GeNN, https://github.com/genn-team/genn) framework is a collection of software aimed at simplifying the simulation of spiking neural networks on GPU accelerators. At its core, GeNN is a meta-compiler that translates model descriptions for spiking neural networks (SNNs) into efficient code for a computational back-end. Currently, GeNN supports CUDA, OpenCL and single-threaded CPU backends. GeNN was designed for maximal user flexibility and so can be employed in computational Neuroscience and machine learning contexts alike. In this talk, I will give an overview of the GeNN ecosystem, discuss some innovations that make important contributions to GeNN's performance, and present benchmark results from Computational Neuroscience and machine learning applications.
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