https://docs.hpc.gwdg.de/services/ftp/neuromorphic-computing/index.htmlNeuromorphic Computing Tools and Libraries SpiNNaker Neuromorphic computing is an alternative way of computing, centered around the concept of the spiking neuron, inspired by the way biological neurons work. It can be used not only to perform simulations of nervous tissue, but also to solve constraint and graph optimization problems, run network simulations, process signals in real time, and perform various AI/ML tasks. Additionally, it is known to require lower energy consumption when compared to more traditional algorithms and computing architectures. For more information, please read the article in the January/February 2024 issue of GWDG News.Hugode-dehttps://docs.hpc.gwdg.de/services/ftp/neuromorphic-computing/nmc-libraries/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.hpc.gwdg.de/services/ftp/neuromorphic-computing/nmc-libraries/index.htmlTo facilitate learning about neuromorphic computing and the development of new neuromorphic algorithms, we host a series of standard neuromorphic libraries and tools as a container. This container is located at /sw/container/neuromorphic/nmc-tools-and-libs.sif, and can be accessed through our JupyterHPC as a custom container, or directly as a container on the cluster through Apptainer. Here is a list and short description of the available tools. If you need new or updated tools, please contact us through our support channels.https://docs.hpc.gwdg.de/services/ftp/neuromorphic-computing/spinnaker/index.htmlMon, 01 Jan 0001 00:00:00 +0000https://docs.hpc.gwdg.de/services/ftp/neuromorphic-computing/spinnaker/index.htmlIntroduction SpiNNaker is the neuromorphic computing architecture from SpiNNcloud. The SpiNNaker architecture has been in development for a couple of decades, with different iterations. We currently host the recently developed SpiNNaker-2 version, and will eventually provide access to 4 boards. SpiNNaker can be used to run typical spiking neuron neuromorphic simulations. This includes simulations of nervous tissue, AI/ML algorithms, graph, constraint, network and optimization problems, signal processing, and control loops. But, additionally, due to its flexible ARM based architecture, it can be made to run other non-neuromorphic simulations (for example, solving distributed systems of partial differential equations).