  Deep Learning Library (DLL) is a new library for machine learning with deep
neural networks that focuses on speed. It supports feed-forward neural networks
such as fully-connected Artificial Neural Networks (ANNs) and Convolutional
Neural Networks (CNNs). It also has very comprehensive support for Restricted
Boltzmann Machines (RBMs) and Convolutional RBMs. Our main motivation for this
work was to propose and evaluate novel software engineering strategies with
potential to accelerate runtime for training and inference. Such strategies are
mostly independent of the underlying deep learning algorithms. On three
different datasets and for four different neural network models, we compared
DLL to five popular deep learning frameworks. Experimentally, it is shown that
the proposed framework is systematically and significantly faster on CPU and
GPU. In terms of classification performance, similar accuracies as the other
frameworks are reported.
