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Compute, Time and Energy Characterization of Encoder-Decoder Networks with Automatic Mixed Precision Training
Deep neural networks have shown great success in many diverse fields. The training of these networks can take significant amounts of time, compute and energy. As datasets get larger and models become more complex, the exploration of model architectures becomes prohibitive. In this paper we examine the compute, energy and …