Energy Consumption in Neural Network Training
The rapid progress of deep learning has led to growing computational requirements, raising concerns about the associated energy consumption. Consequently, energy efficiency becomes an increasingly important research topic. A prerequisite for improving energy efficiency in neural network training is understanding the factors that influence the energy consumption. We provide an overview of current research analyzing the energy consumption in neural network training and discuss key insights and lessons learned toward the development of more energy-efficient training methods.
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Do you need compute time for AI projects? Is your workstation or university cluster too small? Are you considering a project even on the largest scale? The National High Performance Computing Alliance (NHR) and Gauss Centre for Supercomputing (GCS) can help. We provide computing resources well suited for AI in twelve High Performance Computing (HPC) Centres. The best: this is for free for scientists who belong to a German research institution. In our web seminar, we will give an overview over the HPC landscape in Germany, provide an introduction of supercomputer/HPC architecture, and where and how to apply for compute time. We provide a starting point for you to bootstrap your compute time application to a German Tier 1 or 2 HPC Centre.
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