GPU accelerated computing server for artificial intelligence

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We aim to acquire a GPU-accelerated scientific computation server, hosted and operated by the Competence Centre "Artificial Intelligence and Machine Learning" within the Institute for Applied Computer Science (IACS) at the University of Applied Science Stralsund.The aim with the planned GPU-accelerated computation server is three-fold:1. Utilisation of the server through dedicated AI research groups for research, development and application purposes with the aim to speed up training, analysis and operation of comprehensive deep neural networks, thus enabling longer training of bigger networks with more data and more extensive parameter sweeps for parameter optimisation. In particular the server will be used in various research and application areas with the following aims:- to enable the simulation of large networks of biologically more plausible spiking neural networks for the development of new learning algorithms with the promise of transfer to neuromorphic platforms- to support the analysis of deep neural networks in order to make AI explainable for safety-critical applications (as opposed to the usual black box approach)- to simulate large networks of spiking neurons in order to model and simulate cognitive processes and learn from them for acoustic signal processing.- for applications of machine learning in finance, medicine, biotechnology, and industry 4.0, to enable network training with larger data sets in shorter time, and in part to enable network applications in real time. - for Bayesian inference and probabilistic programming: to provide faster simulation, modeling and analysis tools for automatic inference- to analyse video sequence in real-time, for example to enhance automatic speech recognition from lip reading in noisy environments. 2. Cross-sectional support of groups across the institution through the newly founded competence centre "Artificial Intelligence and Machine Learning" within the Institute of Applied Computer Science (IACS). The intention here is to support the uptake of AI techniques also in research groups whose core research and development activities do not centre about AI per se. This is necessary in the light of increasing pervasiveness of AI techniques in industry, research and development.3. Increasing the attraction of the University of Applied Sciences to local and regional small and medium-sized enterprises (SMEs) as the preferred senior partner in AI technology transfer projects. For this it is necessary that the University of Applied Science dispose over competitive computational resources so that there is no need to access external computational resources.