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different hardware backends. Design conventional (GPU-based) deep neural networks for comparison. Publish research articles, regular participation in top international conferences to present your work
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derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development for scalable and green AI. Use cases in
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, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will
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on conventional computing platforms such as GPUs, CPUs and TPUs. As language models become essential tools in society, there is a critical need to optimize their inference for edge and embedded systems
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communication systems which have different performances under different channel conditions. - Providing high-speed underwater communication, for industrial applications in real time underwater communications