Predicting the Computational Cost of Deep Learning Models

Understand

Deep learning is rapidly becoming a go-to tool for many artificial intelligence problems due to its ability to outperform other approaches and even humans at many problems.

• Despite its popularity we are still unable to accurately predict the time it will take to train a deep learning network to solve a given problem.
• This training time can be seen as the product of the training time per epoch and the number of epochs which need to be performed to reach the desired level of accuracy.
• Some work has been carried out to predict the training time for an epoch -- most have been based around the assumption that the training time is linearly related to the number of floating point operations required.