[hpc-announce] CCPE Special Issue on Edge Computing Accelerated Deep Learning - Technologies and Applications

[Xiao Liu]

Concurrency and Computation: Practice and Experience Special Issue on Edge Computing Accelerated Deep Learning - Technologies and Applications

The goal of this special issue is to promote innovative technologies and applications that accelerate deep learning in the distributed edge computing environment, including 1) accelerating deep neural network (DNN) inference to reduce latency and avoid interruptions, 2) accelerating the convergence of DNN training in the distributed environment, e.g., federated learning, gossip training, etc., 3) accelerating the system performance of DNN applications with low latency and enhanced privacy, 4) innovative edge-enabled deep learning applications in the areas such as smart health, smart traffic, autonomous vehicles, business, and scientific workflows, etc.

This Special Issue on "Edge Computing Accelerated Deep Learning - Technologies and Applications" is open for submission, and includes high-quality papers presented at a workshop on "5th International Workshop on Emerging Computing Paradigms and Context in Business Process Management - CCBPM2020" in CCGrid2020 to be held in Melbourne, Australia, 11-14 May 2020.

The guest editors of the special issue are:
. Dr. Xiao Liu Deakin University, Melbourne, Australia.
. Dr. Dong Yuan The University of Sydney, Sydney, Australia.
. Prof. Xiaowen Chu Hong Kong Baptist University, Hong Kong, China.
. A/Prof. Honggang Zhang University of Massachusetts Boston, Boston, USA.

Topics of interest include, but are not limited to, the following scope:
. System architectures and applications for deep learning in edge computing
. Modeling, analysis, and measurement for deep learning in edge computing
. Algorithms and systems for accelerating deep learning inference in edge computing
. Distributed algorithms for training deep learning models in edge computing
. Deep learning-based networking and communication protocols for edge computing
. Deep learning in mobile edge computing
. Resource management and scheduling for deep learning application at the edge
. Security and privacy of deep learning applications in edge computing
. Programming models and toolkits for deep learning in edge computing

Submissions must follow these guidelines:
. Please submit your paper to Manuscript central as the CCBPM2020 special issue.
. Submissions should be prepared for publication according to the journal submission guidelines
. The submitted papers must have at least 50% different material beyond any other previously published work
. Submissions should be up to 16 pages in length (inclusive of figures and tables)
. There is a limit of 15 papers plus an editorial in the special issue

Important dates:
. Paper Submission Deadline: 30 October 2020
. First-Round Notification: 30 December 2020
. Revision Submission: 28 February 2021
. Final Decision: 31 March 2021
. Publication Date: 2021

_________________________
Dr. Xiao Liu BMgt, MMgt, PhD, SMIEEE
Associate Head Of School (International) and Senior Lecturer
School of Information Technology,
Deakin University, 221 Burwood Hwy, Burwood, VIC 3125.
www.deakin.edu.au
Deakin University CRICOS Provider Code 00113B

Ph: +61 3 92445428
e: xiao.liu at deakin.edu.au
office: T2.19, Melbourne Burwood Campus
w: https://sites.google.com/site/drxiaoliu/


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