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IEEE — 2019
Deep Learning at Scale
Iacopo Colonnelli
Abstract
This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed from both a parallel computing and deep learning perspective, then a different approach is presented that is meant to allow DNN training to scale while retaining good convergence properties. Moreover, an experimental implementation is presented as well as some preliminary results.
@misc{19:PDPNNT,
abstract = {This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed from both a parallel computing and deep learning perspective, then a different approach is presented that is meant to allow DNN training to scale while retaining good convergence properties. Moreover, an experimental implementation is presented as well as some preliminary results.},
address = {Pavia, Italy},
author = {Iacopo Colonnelli},
howpublished = {27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019)},
month = feb,
publisher = {{IEEE}},
title = {Deep Learning at Scale},
url = {https://datacloud.di.unito.it/index.php/s/nRW9M69C3AtpDoM},
year = {2019},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/nRW9M69C3AtpDoM}}