DLL : a fast deep neural network library

Wicht, Baptiste (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland ; University of Fribourg, Fribourg, Switzerland) ; Fischer, Andreas (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland ; University of Fribourg, Fribourg, Switzerland) ; Hennebert, Jean (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland ; University of Fribourg, Fribourg, Switzerland)

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.


Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
iCoSys - Institut des systèmes complexes
Subject(s):
Ingénierie
Publisher:
Cham, Springer
Date:
2018-08
Cham
Springer
Pagination:
12 p.
Published in:
Lecture Notes in Computer Science
Author of the book:
Wicht, Baptiste ; School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland ; University of Fribourg, Fribourg, Switzerland
Fischer, Andreas ; School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland ; University of Fribourg, Fribourg, Switzerland
Hennebert, Jean ; School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland ; University of Fribourg, Fribourg, Switzerland
DOI:
ISSN:
0302-9743
ISBN:
978-3-319-99977-7
Appears in Collection:

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 Record created 2019-02-26, last modified 2019-03-05

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