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Abstract

Deep Learning Library (DLL) is a library for machine learning with deep neural networks that focuses on speed. It supports feedforward neural networks such as fully-connected Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs). 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 libraries. Experimentally, it is shown that the proposed library is systematically and significantly faster on CPU and GPU. In terms of classification performance, similar accuracies as the other libraries are reported.

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