  Many complex problems, such as natural language processing or visual object
detection, are solved using deep learning. However, efficient training of
complex deep convolutional neural networks for large data sets is
computationally demanding and requires parallel computing resources. In this
paper, we present two parameterized performance models for estimation of
execution time of training convolutional neural networks on the Intel many
integrated core architecture. While for the first performance model we
minimally use measurement techniques for parameter value estimation, in the
second model we estimate more parameters based on measurements. We evaluate the
prediction accuracy of performance models in the context of training three
different convolutional neural network architectures on the Intel Xeon Phi. The
achieved average performance prediction accuracy is about 15% for the first
model and 11% for second model.
