@inproceedings{61e543ba2611421f9799c8daa539982a,
title = "Parameterized Structured Pruning for Deep Neural Networks",
abstract = "As a result of the growing size of Deep Neural Networks (DNNs), the gap to hardware capabilities in terms of memory and compute increases. To effectively compress DNNs, quantization and pruning are usually considered. However, unconstrained pruning usually leads to unstructured parallelism, which maps poorly to massively parallel processors, and substantially reduces the efficiency of general-purpose processors. Similar applies to quantization, which often requires dedicated hardware. We propose Parameterized Structured Pruning (PSP), a novel technique to dynamically learn the shape of DNNs through structured sparsity. PSP parameterizes structures (e.g. channel- or layer-wise) in a weight tensor and leverages weight decay to learn a clear distinction between important and unimportant structures. As a result, PSP maintains prediction performance, creates a substantial amount of sparsity that is structured and, thus, easy and efficient to map to a variety of massively parallel processors, which are mandatory for utmost compute power and energy efficiency.",
author = "G{\"u}nther Schindler and Wolfgang Roth and Franz Pernkopf and Holger Fr{\"o}ning",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-64580-9_3",
language = "English",
isbn = "978-3-030-64579-3",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Cham",
pages = "16--27",
editor = "Giuseppe Nicosia and Varun Ojha and {La Malfa}, Emanuele and Giorgio Jansen and Vincenzo Sciacca and Panos Pardalos and Giovanni Giuffrida and Renato Umeton",
booktitle = "Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Revised Selected Papers",
note = "6th International Conference on Machine Learning, Optimization, and Data Science : LOD 2020, LOD 2020 ; Conference date: 19-07-2020 Through 23-07-2020",
}