Recently, Deep Learning (DL) has received tremendous attention in the research community because of the impressive results obtained for a large number of machine learning problems. The success of state-of-the-art deep learning systems relies on training deep neural networks over a massive amount of training data, which typically requires a large-scale distributed computing infrastructure to run. In order to run these jobs in a scalable and efficient manner, on cloud infrastructure or dedicated HPC systems, several interesting research topics have emerged which are specific to DL. The sheer size and complexity of deep learning models when trained over a large amount of data makes them harder to converge in a reasonable amount of time. It demands advancement along multiple research directions such as, model/data parallelism, model/data compression, distributed optimization algorithms for DL convergence, synchronization strategies, efficient communication and specific hardware acceleration.
SCADL seeks to advance the following research directions:
This intersection of distributed/parallel computing and deep learning is becoming critical and demands specific attention to address the above topics which some of the broader forums may not be able to provide. The aim of this workshop is to foster collaboration among researchers from distributed/parallel computing and deep learning communities to share the relevant topics as well as results of the current approaches lying at the intersection of these areas.
In this workshop, we solicit research papers focused on distributed deep learning aiming to achieve efficiency and scalability for deep learning jobs over distributed and parallel systems. Papers focusing both on algorithms as well as systems are welcome. We invite authors to submit papers on topics including but not limited to:
ScaDL 2020 accepts submissions in three categories:
The aforementioned lengths include all technical content, references and appendices.
Papers should be formatted using IEEE conference style, including figures, tables, and references. The IEEE conference style templates for MS Word and LaTeX provided by IEEE eXpress Conference Publishing are available for download. See the latest versions at https://www.ieee.org/conferences/publishing/templates.html
https://easychair.org/conferences/?conf=scadl2020
Submission deadline: Feb 14, 2020
Notifications: March 6, 2020
Camera Ready deadline: March 15, 2020
ScaDL 2020 will feature two exciting keynote speakers : Dr. Manish Gupta, Director of Google Research, India and Prof. Geoffrey Fox, Distinguished Professor at Indiana University, USA.
Christopher Carothers, RPI, USA
Ashish Verma, IBM Research AI, USA
K. R. Jayaram, IBM Research AI, USA
Parijat Dube, IBM Research AI, USA
Kangwook Lee, U Wisconsin, Madison, USA
Li Zhang, IBM Research, USA
Xiangru Lian, U Rochester, USA
Eduardo Rocha Rodrigues, IBM, Brazil
Wagner Meira Jr., UFMG, Brazil
Stacy Patterson, RPI, USA
Alex Gittens, RPI, USA
Catherine Schuman, ORNL, USA
Ignacio Blanquer, UPV, Spain
Leandro Balby Marinho, UFCG, Brazil
Chen Wang, IBM Research, USA
Danilo Ardagna, Politecnico di Milano, Italy
Vijay K. Garg, University of Texas at Austin
Vinod Muthusamy, IBM Research AI
Yogish Sabharwal, IBM Research AI
Danilo Ardagna, Politecnico di Milano