CALL FOR PAPERS
IEEE Journal of Selected Topics in Signal Processing Special Issue on
Compact Deep Neural Networks with Industrial Applications
https://ift.tt/2Uazrwf
Artificial neural networks have been adopted for a broad range of
tasks in areas like multimedia analysis and processing, media coding,
data analytics, etc. Their recent success is based on the feasibility
of processing much larger and complex deep neural networks (DNNs) than
in the past, and the availability of large-scale training data sets.
As a consequence, the large memory footprint of trained neural
networks and the high computational complexity of performing inference
cannot be neglected. Many applications require the deployment of a
particular trained network instance, potentially to a larger number of
devices, which may have limitations in terms of processing power and
memory e.g., for mobile devices or Internet of Things (IoT) devices.
For such applications, compact representations of neural networks are
of increasing relevance.
This special issue aims to feature recent work related to techniques
and applications of compact and efficient neural network
representations. It is expected that these works will be of interest
to both academic researchers and industrial practitioners, in the
fields of machine learning, computer vision and pattern recognition,
media data processing, as well as fields such as AI hardware design
etc. In spite of active research in the area, there are still open
questions to be clarified concerning, for example, how to train neural
networks with optimal performance while achieving compact
representations, and how to achieve representations that do not only
allow for compact transmission, but also for efficient inference.
This special issue therefore solicits original and innovative works to
address these open questions in, but not limited to, following topics:
● Sparsification, binarization, quantization, pruning, thresholding
and coding of neural networks
● Efficient computation and acceleration of deep convolutional neural networks
● Deep neural network computation for low power consumption applications
● Exchange formats and industrial standardization of compact &
efficient neural networks
● Applications e.g. video & media compression methods using compressed DNNs
● Performance evaluation and benchmarking of compressed DNNs
Prospective authors should follow the instructions given on the IEEE
JSTSP webpage: https://ift.tt/2JCxkNO,
and submit their manuscript through the web submission system at:
https://ift.tt/YHx6Zd.
Dates:
Submission deadline: 01-Jun-2019
First Review: 01-Aug-2019
Revisions due: 01-Oct-2019
Second Review: 15-Nov-2019
Final Manuscripts: 10-Jan-2020
Publication: March 2020
Guest Editors:
Diana Marculescu, Carnegie Mellon University, USA
Lixin Fan, JD.COM, Silicon Valley Labs, USA (Lead GE)
Werner Bailer, Joanneum Research, Austria
Yurong Chen, Intel Labs China, China