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ScaDL 2019 : Scalable Deep Learning over Parallel and Distributed Infrastructures | |||||||||||||||
Link: https://sites.google.com/site/scadlworkshop/ | |||||||||||||||
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Call For Papers | |||||||||||||||
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 8.5x11 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 |
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