What is the importance of model compression?

A major reason why more researchers have turned towards Model compression is the difficulty in deploying neural networks and deep learning models on systems with limited hardware resources. While these models have been successful in making headlines and achieving extraordinary performances, they require the support of expensive, high speed GPUs to get them working, which limits their applications. Being able to compress these highly complex models, transfer them to hardware devices and end their dependence on huge computational resources is one of the major goals of this domain. These kind of advances can help us incorporate AI into every small embedded system around us.

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