Pushing the boundaries of parallel Deep Learning -- A practical approach

Autor:	Viviani, Paolo, Drocco, Maurizio, Aldinucci, Marco
Rok vydání:	2018
Předmět:	Computer Science - Distributed Parallel and Cluster Computing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This work aims to assess the state of the art of data parallel deep neural network training, trying to identify potential research tracks to be exploited for performance improvement. Beside, it presents a design for a practical C++ library dedicated at implementing and unifying the current state of the art methodologies for parallel training in a performance-conscious framework, allowing the user to explore novel strategies without departing significantly from its usual work-flow. Comment: 12 pages, 4 figures, 61 references
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1806.09528 Zobrazit plný text záznamu View this record from Arxiv