Scalable Deep Learning over Parallel and Distributed Infrastructures

Event Dates

May 24, 2019 - May 24, 2019

Location

Rio de Janeiro

Submission Deadline

Feb 18, 2019

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:

Deep learning on HPC systems

Deep learning for edge devices

Model-parallel and data-parallel techniques

Asynchronous SGD for Training DNNs

Communication-Efficient Training of DNNs

Model/data/gradient compression

Learning in Resource constrained environments

Coding Techniques for Straggler Mitigation

Elasticity for deep learning jobs/spot market enablement

Hyper-parameter tuning for deep learning jobs

Hardware Acceleration for Deep Learning

Scalability of deep learning jobs on large number of nodes

Deep learning on heterogeneous infrastructure

Efficient and Scalable Inference

Data storage/access in shared networks for deep learning jobs

Author Instructions

Submitted manuscripts may not exceed ten (10) single-spaced double-column pages using 10-point size font on 8.5×11 inch pages (IEEE conference style), including figures, tables, and references. The submitted manuscripts should include author names and affiliations. 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

Use the following link for submissions: https://easychair.org/conferences/?conf=scadl2019

Organizing Committee

General Chairs

Gauri Joshi, Carnegie Mellon University (gaurij@andrew.cmu.edu)

Ashish Verma, IBM Research AI (ashish.verma1@ibm.com)

Program Chairs

Yogish Sabharwal, IBM Research AI

Parijat Dube, IBM Research AI

Local Chair

Eduardo Rodrigues, IBM Research

Steering Committee

Vijay K. Garg, University of Texas at Austin

Vinod Muthuswamy, IBM Research AI

Technical Program Committee

Alvaro Coutinho – Federal University of Rio de Janeiro

Dimitris Papailiopoulos, University of of Wisconsin-Madison

Esteban Meneses, Costa Rica Institute of Technology

Kangwook Lee, KAIST

Li Zhang, IBM Research

Lydia Chen, TU Delft

Philippe Navaux, University of Rio Grande do Sul

Rahul Garg, Indian Institute of Technology Delhi

Vikas Sindhwani, Google Brain

Wei Zhang, IBM Research

Xiangru Lian, University of Rochester

Key Dates

Paper Submission January 25, 2019

Acceptance Notification February 25, 2019

Camera-ready due March 15, 2019