Categories |
PARALLEL COMPUTING
DEEP LEARNING
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About |
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. |
Call for Papers |
Areas of InterestIn 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:
Author InstructionsScaDL 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 Submission Linkhttps://easychair.org/conferences/?conf=scadl2020 DeadlinesSubmission deadline: Feb 1, 2020 Notifications: Feb 28, 2020 Camera Ready deadline: March 15, 2020 General ChairsChristopher Carothers, RPI, USA Ashish Verma, IBM Research AI, USA Program Committee ChairsK. R. Jayaram, IBM Research AI, USA Parijat Dube, IBM Research AI, USA Program CommitteeKangwook Lee, KAIST, Korea 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 Publicity ChairDanilo Ardagna, Politecnico di Milano, Italy Steering CommitteeVijay K. Garg, University of Texas at Austin Vinod Muthusamy, IBM Research AI Yogish Sabharwal, IBM Research AI Danilo Ardagna, Politecnico di Milano |
Summary |
ScaDL 2020 : Scalable Deep Learning over Parallel And Distributed Infrastructures will take place in New Orleans. It’s a 1 day event starting on May 22, 2020 (Friday) and will be winded up on May 22, 2020 (Friday). ScaDL 2020 falls under the following areas: PARALLEL COMPUTING, DEEP LEARNING, etc. Submissions for this Workshop can be made by Feb 01, 2020. Authors can expect the result of submission by Feb 28, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before Mar 15, 2020 to the official website of the Workshop. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the ScaDL 2020
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Credits and Sources |
[1] ScaDL 2020 : Scalable Deep Learning over Parallel And Distributed Infrastructures |