Parameter sharing is a term from the fields of artificial intelligence, big data and smart data as well as Industry and Factory 4.0. It refers to a method in which several parts of a system use the same settings instead of saving separate parameters for each unit. This leads to lower memory requirements and faster calculations.
An illustrative example of this can be found in artificial neural networks, which are used to analyse images, for example. Here, many image areas can be analysed according to the same pattern. The learning system "shares" the same parameters again and again in order to recognise certain shapes or patterns. This makes the neural network much more efficient, requires fewer resources and works faster - a great advantage, especially when huge amounts of data need to be analysed.
Parameter sharing therefore helps to make artificial intelligence and automation more cost-effective and scalable. Costs and energy can be saved, particularly in production halls or in real-time analyses of large image data. For companies, this means that parameter sharing keeps high-tech solutions affordable and flexible to use.
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